TensorFlow Jobs in UAE

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Shape the Future of<strong>&nbsp;AI, Engineering &amp; Digital Transformation</strong><br> <br> We're building a network of exceptional professionals for upcoming enterprise-scale technology,&nbsp;<strong>AI, digital transformation, and energy sector initiatives&nbsp;</strong>across the UAE and the wider GCC. Whether you're actively exploring new opportunities or simply interested in staying connected, we'd love to hear from you. By joining our Talent Community, you'll be considered for future projects and permanent opportunities across a range of high-impact programmes.<br> <br> Areas of Expertise AI &amp; Computer Vision <ul> <li>Lead Data Scientists </li><li>Data Scientists </li><li>AI Engineers </li><li>Machine Learning Engineers </li><li>Computer Vision Specialists </li><li>Data Annotators </li></ul> <br> <strong>Skills of interest</strong>&nbsp;Computer Vision, Video Analytics, PyTorch, TensorFlow, OpenCV, YOLO, MLOps, LLMs, RAG, Agentic AI, Edge AI, Data Engineering.We're building a network of exceptional professionals for upcoming enterprise-scale technology,&nbsp;<strong>AI, digital transformation, and energy sector initiatives&nbsp;</strong>across the UAE and the wider GCC. Whether you're actively exploring new opportunities or simply interested in staying connected, we'd love to hear from you. By joining our Talent Community, you'll be considered for future projects and permanent opportunities across a range of high-impact programmes.<br> <br> Areas of Expertise AI &amp; Computer Vision <ul> <li>Lead Data Scientists </li><li>Data Scientists </li><li>AI Engineers </li><li>Machine Learning Engineers </li><li>Computer Vision Specialists </li><li>Data Annotators </li></ul> <br> <strong>Skills of interest</strong>&nbsp;Computer Vision, Video Analytics, PyTorch, TensorFlow, OpenCV, YOLO, MLOps, LLMs, RAG, Agentic AI, Edge AI, Data Engineering. <br><br> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><ul><li><p>Develop AI models and algorithms for various applications.</p></li><li><p>Implement machine learning solutions to solve complex problems.</p></li><li><p>Collaborate with cross-functional teams to design and deploy AI-powered systems.</p></li><li><p>Analyze and interpret large datasets to extract useful insights.</p></li><li><p>Stay current with the latest trends and advancements in AI and machine learning technologies.</p></li></ul></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><ul><li><p>Bachelor's or Master's degree in Computer Science, Engineering, or related field.</p></li><li><p>Solid understanding of machine learning algorithms and principles.</p></li><li><p>Proficiency in programming languages such as Python, Java, or C++.</p></li><li><p>Experience with AI frameworks like TensorFlow, PyTorch, or scikit-learn.</p></li><li><p>Strong analytical and problem-solving skills.</p></li></ul><p></p></section>

<ul><li><p>he AI Engineer is responsible for developing, testing, and deploying AI/ML and Generative AI solutions, including LLM-based applications, RAG pipelines, and Agentic workflows. This role also includes the ability to deploy and run LLMs on local/on-premise servers, supporting secure and efficient AI solution delivery in enterprise environments. </p></li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li><p>Bachelor’s degree in Computer Science, AI, Data Science, or related field. 2+ years of experience in AI/ML development. Strong programming skills in Python. Hands-on experience with: o Machine learning frameworks (Scikit-learn, PyTorch, or TensorFlow) o Basic NLP concepts Understanding of ML model lifecycle (training, evaluation, deployment). Familiarity with REST APIs and integration concepts. Experience with Git/version control.</p></li></ul>

<p>Builds and validates predictive and generative models, and turns data into decisions clients can act on.&nbsp;</p><p>&nbsp;</p><p><strong>What you'll do&nbsp;</strong></p><ul><li>Design and validate machine learning models for prediction, classification, and forecasting&nbsp;</li><li>Partner with engineering to move models from notebook to production&nbsp;</li><li>Analyze structured and unstructured data to surface insight for client decisions&nbsp;</li><li>Build and maintain the data pipelines that feed AI models&nbsp;</li><li>Present findings and model performance clearly to technical and non-technical stakeholders&nbsp;</li></ul><p>&nbsp;</p><p><strong>What you bring&nbsp;</strong></p><ul><li>3+ years in data science or applied machine learning, with a quantitative degree or equivalent experience&nbsp;</li><li>Strong Python (pandas, scikit-learn) and SQL&nbsp;</li><li>Experience with a deep learning framework such as PyTorch or TensorFlow&nbsp;</li><li>Solid grounding in statistical methods and experiment design&nbsp;</li><li>Ability to explain findings clearly to non-technical audiences&nbsp;</li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <br><br>This role sits at the forefront of AI innovation, focusing on the development, training and optimisation of large language models and AI systems. You will work with state-of-the-art open-source frameworks and contribute to high-impact AI initiatives.<br><br>Client Details<br><br>A highly innovative financial services organisation investing in advanced technology and AI-driven solutions to solve complex real-world challenges.<br><br> <br><br>Description<br>Train, fine-tune and optimise AI models to improve performance, efficiency and scalability.Build high-quality datasets from user interactions, business use cases and real-world applications.Design and enhance model training and inference frameworks using leading open-source technologies.Adapt and improve existing training pipelines to support evolving AI products and platforms.Research emerging AI techniques and contribute to the advancement of next-generation LLM solutions.<br><br><br>Profile<br>Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning or a related discipline.Strong understanding of model architectures, training methodologies and inference frameworks.Hands-on experience with large language models and open-source ecosystems, including DeepSeek, Gemma or similar technologies.Knowledge of training frameworks such as LLaMA Factory, VERL, PyTorch, TensorFlow or Hugging Face.Proven ability to modify, optimise and deploy AI training pipelines in production environments.<br><br><br>Job Offer<br>Opportunity to work on leading-edge AI research and development initiatives.Collaborative environment with access to advanced technologies and highly skilled teams.Competitive compensation package, comprehensive benefits and excellent long-term career growth opportunities.<br><br> </div><h2 class="h5">Skills</h2> <div data-jb-field="skills">fine-tune, training, model. LLM<br> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Job description <p>​</p><br><p><b>Discover the Opportunity</b></p><br><p>We are hiring a visionary Principal Manager of Engineering to lead cutting edge squads for an industry leader in artificial intelligence. Our client bridges the gap between advanced research and real world impact by building secure enterprise applications for knowledge workers. As a foundational member of the team you will work in a world class environment with access to the latest tools and technologies.</p><br><p><br><b>Discover the Role</b></p><br><p>In this position you will lead and mentor a highly skilled team of front end back end and machine learning engineers. You will drive the development of solutions leveraging next generation AI and LLM technologies while collaborating directly with customers to deploy these systems onto their tech stacks. This role requires a deep understanding of machine learning along with the ability to manage project timelines and budgets effectively.</p><br><p>​</p><br><p><b>Discover the Responsibilities</b></p><br><ul><li><p>Lead a multidisciplinary team of engineers to deliver complex AI projects.</p><br></li><li><p>Define and implement best practices for AI model development and deployment.</p><br></li><li><p>Contribute to the overarching AI strategy and roadmap for the company.</p><br></li><li><p>Lead technical discussions with customers and translate complex concepts to non technical stakeholders.</p><br></li><li><p>Stay abreast of the latest research and industry trends to foster a culture of innovation.</p><br></li><li><p>Ensure ethical AI development while solving challenges in critical sectors.</p><br></li></ul><p>​</p><br><p><b>Discover the Requirements</b></p><br><ul><li><p>MS or PhD in Computer Science Machine Learning or a related field.</p><br></li><li><p>5 or more years of experience in AI or ML engineering 2 or more years of experience in a leadership role.</p><br></li><li><p>Deep expertise in natural language processing and deep learning.</p><br></li><li><p>Hands on experience with Transformer based models and LLM architectures.</p><br></li><li><p>Strong Python programming skills and familiarity with frameworks like PyTorch or TensorFlow Experience with large scale distributed computing and ML infrastructure.</p><br></li><li><p>A track record of publications or patents in the field is a plus.</p><br></li></ul><br> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p>Card Fraud Model Development &amp; Optimization Design, develop, and optimize card fraud detection models for issuing and acquiring portfolios. Implement advanced statistical, machine learning, and AI techniques to improve fraud detection accuracy and precision and minimizing false positives. Model Hosting &amp; Deployment Deploy models using SAS SFD and integrate with production systems. Ensure seamless hosting and scalability of models across multiple environments. MLOps &amp; Automation Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models. Automate model retraining and performance tracking processes. Evaluation &amp; Testing Conduct rigorous model validation, stress testing, and performance benchmarking. Collaborate with fraud operations teams to ensure models meet business and regulatory requirements. Collaboration &amp; Stakeholder Management Partner with fraud risk, data engineers, IT and business teams to deliver end-to-end solutions. Communicate insights and recommendations to senior management and business stakeholders.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><p>Education Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related field. Technical Skills Strong proficiency in SAS (including SAS SFD), Python, and SQL. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit- learn). Hands-on experience with MLOps tools and practices (e.g., MLflow,Kubeflow, CI/CD pipelines). Deep understanding of card fraud detection techniques and transaction data. Experience 7+ years in data science roles, with at least 3 years in card fraud modeling. Proven track record of deploying models in production environments. Soft Skills Strong analytical and problem-solving skills. Excellent communication and stakeholder management abilities. <strong>Preferred Qualifications</strong> Experience in banking or payments industry. Familiarity with cloud platforms (AWS, Azure, GCP) for model hosting and deployment. Knowledge of regulatory compliance in fraud risk management.</p><p></p></section>

<ul><li><p>We are looking for an experienced AI Engineer responsible for designing, developing, and deploying Generative AI and Agentic AI solutions that support enterprise-scale business use cases. The role will build intelligent systems that integrate complex backend services and client-facing applications across web, mobile and enterprise platforms. The primary responsibility is to design and develop AI-powered applications, autonomous agents and multi-agent workflows while coordinating with cross-functional teams across architecture, engineering, product and business functions. A commitment to collaborative problem solving, sophisticated design and product quality is essential. This role requires strong hands-on engineering experience, practical working knowledge of modern agent frameworks, and the ability to deliver secure, scalable, observable and governed AI solutions in cloud-native environments.</p></li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li><p>Minimum Qualification</p><ul><li><p>Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence or a related discipline.</p></li><li><p>Relevant cloud, AI engineering, machine learning or architecture certifications are preferred.</p></li></ul><p>Minimum Experience</p><ul><li><p>Senior professional with around 10 years of total software engineering, architecture, cloud or platform engineering experience.</p></li><li><p>Minimum 3+ years of relevant hands-on AI Engineering experience, including Generative AI and practical LLM-based application delivery.</p></li><li><p>Strong proficiency in Python, including NumPy, pandas, FastAPI and hands-on experience with PyTorch or TensorFlow.</p></li><li><p>Hands-on experience with LangChain and LangGraph; mandatory working experience with Microsoft Semantic Kernel and Microsoft AutoGen.</p></li><li><p>Experience implementing RAG using embeddings, vector databases, semantic search, retrieval optimization and model evaluation techniques.</p></li><li><p>Experience deploying and managing models using Amazon Bedrock, Azure OpenAI Service and Google Vertex AI.</p></li><li><p>Hands-on experience with microservices, containers, APIs, event-driven architecture, cloud-native services and evolutionary architecture practices.</p></li><li><p>Experience managing and deploying AI workloads on Kubernetes in cloud-native and/or hybrid environments.</p></li><li><p>Experience with CI/CD tools such as Jenkins or GitLab, DevOps toolchains, configuration management and cloud/on-prem deployment pipelines.</p></li><li><p>Experience setting up pipelines with static code analysis, requirement tagging in Jira, quality gates and release governance.</p></li><li><p>Experience operating monitoring tools for traditional infrastructure, cloud environments and AI-enabled business applications.</p></li><li><p>Strong hands-on problem-solving mindset with the ability to analyze trade-offs and deliver sustainable, secure and high-quality solutions.</p></li></ul><p>Key Technical Skills</p><ul><li><p>Generative AI, Agentic AI, autonomous agents, multi-agent orchestration and workflow-based AI systems.</p></li><li><p>LLMs, embeddings, vector databases, RAG, semantic search, model evaluation, guardrails, observability and AI governance.</p></li><li><p>Semantic Kernel, AutoGen, LangChain, LangGraph and similar agent frameworks.</p></li><li><p>Python, FastAPI, PyTorch/TensorFlow, REST APIs, microservices, serverless functions and event-driven integration.</p></li><li><p>Azure, AWS, Kubernetes, containers, CI/CD, DevOps automation, monitoring and secure software delivery.</p></li></ul><p>Behavioural / Leadership Skills</p><ul><li><p>Strong collaborative mindset for agile architecture and decentralized decision making.</p></li><li><p>Proactive, positive and growth-oriented leadership style with the ability to motivate engineers and foster craftsmanship.</p></li><li><p>Strong communication, stakeholder engagement and influencing skills across product, business, architecture and engineering teams.</p></li><li><p>Analytical, system-thinking and pragmatic problem-solving approach with commitment to product quality.</p></li></ul><p>Skills</p><p>Generative AI</p><p>Python</p><p>Kubernetes</p></li></ul>

<ul><li><p> The AI Engineer is responsible for developing, testing, and deploying AI/ML and Generative AI solutions, including LLM-based applications, RAG pipelines, and Agentic workflows. This role also includes the ability to deploy and run LLMs on local/on-premise servers, supporting secure and efficient AI solution delivery in enterprise environments. </p></li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li><p>Bachelor’s degree in Computer Science, AI, Data Science, or related field. 2+ years of experience in AI/ML development. Strong programming skills in Python. Hands-on experience with: o Machine learning frameworks (Scikit-learn, PyTorch, or TensorFlow) o Basic NLP concepts Understanding of ML model lifecycle (training, evaluation, deployment). Familiarity with REST APIs and integration concepts. Experience with Git/version control.</p></li></ul>

<p>Are you passionate about Artificial Intelligence and ready to make an impact? We are looking for talented Emirati professionals to join our team in Abu Dhabi, UAE.</p><p><br><strong>Available Positions:</strong></p><ul><li><p>Machine Learning Engineer</p></li><li><p>Data Scientist</p></li><li><p>AI Software Developer</p></li></ul><p><strong>Desired Candidate Profile</strong></p><ul><li><p>Bachelor’s degree in Computer Engineering, Software Engineering, Computer Science, or a related field.</p></li><li><p>Completion of relevant AI/ML training programs or certifications, including platforms such as AWS Machine Learning, Azure AI, or Google AI, demonstrating applied knowledge and expertise.</p></li><li><p>Minimum 2 years of progressive experience working on AI/ML projects, including model development, deployment, and implementation within the technology sector or related industries.</p></li><li><p>Strong programming skills in Python, R, or Scala, with practical experience using AI frameworks such as TensorFlow, PyTorch, or scikit-learn.</p></li><li><p>Willing to work onsite in Abu Dhabi.</p></li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span><strong>Role Overview</strong></span><br>We are looking for an&nbsp;<strong>NLP Engineer</strong>&nbsp;to lead the design, development, and deployment of production-grade NLP and Generative AI solutions. You will own complex AI initiatives end-to-end, drive technical architecture, and help shape our NLP/LLM engineering practices.<p><strong>Key Responsibilities</strong></p><br><br><ul><li>Lead the design and development of scalable NLP and LLM-powered solutions.</li><li>Architect and optimize RAG pipelines, embeddings, vector search, and LLM applications.</li><li>Fine-tune, evaluate, and optimize transformer-based models and LLMs for production use.</li><li>Build robust data, training, inference, and model-evaluation pipelines.</li><li>Drive improvements in model quality, latency, scalability, reliability, and cost.</li><li>Establish best practices for experimentation, evaluation, deployment, and monitoring of AI systems.</li><li>Translate complex business requirements into effective AI/ML solutions.</li><li>Collaborate closely with ML engineers, software engineers, product teams, and stakeholders.</li><li>Mentor engineers and provide technical leadership across NLP/Generative AI initiatives.</li><li>Stay current with emerging research, models, frameworks, and best practices in NLP and Generative AI.</li></ul><p><strong>Requirements</strong></p><br><br><ul><li>5+ years of professional experience in NLP, Machine Learning, or Generative AI.</li><li>Strong Python programming and hands-on experience with PyTorch, TensorFlow, Hugging Face, or equivalent frameworks.</li><li>Deep understanding of NLP, transformers, LLMs, embeddings, RAG, fine-tuning, and model evaluation.</li><li>Proven experience taking AI/ML solutions from experimentation to production.</li><li>Strong software engineering and system-design skills, including APIs, data pipelines, and scalable architectures.</li><li>Strong analytical and problem-solving skills with the ability to independently drive technical initiatives.</li></ul><p><strong>Preferred</strong></p><br><br><ul><li>Experience with PEFT/LoRA, quantization, inference optimization, or distributed model training.</li><li>Experience with vector databases, MLOps, cloud platforms, and AI evaluation/observability frameworks.</li><li>Experience building AI agents, multimodal systems, or other advanced Generative AI applications.</li><li>Contributions to open-source projects, research, publications, or applied AI/ML innovation.</li></ul> <br><br> </div>

IMPORTANT: Please ensure that relevant AI-related skills are included in appropriate external job postings and coordinate this in advance with your hiring manager. Be aware that the percentage of externally posted jobs featuring AI skills is monitored (KPI “Job Posting AI-Readiness”) and is included in regular OE reports as part of Allianz’s workforce transformation. If you have any questions, please contact your Head of Recruiting/Talent Acquisition.<br><br><strong>AI-related Skills/terms Relevant For All Roles (examples)<br><br></strong><ul><li>(Generative) Artificial Intelligence or GenAI or AI</li><li>Data Analysis</li><li>(Microsoft) Copilot</li><li>ChatGPT<br><br></li></ul><strong>AI-related Skills/terms Relevant For All Data/Tech Roles (examples)<br><br></strong><ul><li>Python</li><li>TensorFlow</li><li>PyTorch</li><li>Language modeling</li><li>Natural Language Processing (NLP)</li><li>Machine Learning<br><br></li></ul>105114 | Customer Services &amp; Claims | Professional | Allianz Partners | Full-Time | Permanent<br><br>Warning: When posting this job advertisment on an external job board, the length of the following fields combined must not exceed 3950 characters: "External Posting Description", "External Posting Footer"<br><br>xxx

IMPORTANT: Please ensure that relevant AI-related skills are included in appropriate external job postings and coordinate this in advance with your hiring manager. Be aware that the percentage of externally posted jobs featuring AI skills is monitored (KPI “Job Posting AI-Readiness”) and is included in regular OE reports as part of Allianz’s workforce transformation. If you have any questions, please contact your Head of Recruiting/Talent Acquisition.<br><br><strong>AI-related Skills/terms Relevant For All Roles (examples)<br><br></strong><ul><li>(Generative) Artificial Intelligence or GenAI or AI</li><li>Data Analysis</li><li>(Microsoft) Copilot</li><li>ChatGPT<br><br></li></ul><strong>AI-related Skills/terms Relevant For All Data/Tech Roles (examples)<br><br></strong><ul><li>Python</li><li>TensorFlow</li><li>PyTorch</li><li>Language modeling</li><li>Natural Language Processing (NLP)</li><li>Machine Learning<br><br></li></ul>98862 | Customer Services &amp; Claims | Professional | Allianz Partners | Full-Time | Permanent<br><br>Warning: When posting this job advertisment on an external job board, the length of the following fields combined must not exceed 3950 characters: "External Posting Description", "External Posting Footer"<br><br>xxxx

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> Job description <p>​</p><br><p>We are looking for a Senior Tech Lead with exceptional depth in AI, data systems, and product innovation.</p><br><p>This is not a traditional software engineering management role. We are seeking a world-class thinker and builder who is passionate about challenging legacy incumbents, leading deep technical exploration, and shipping real products that change the game.<br></p><br><p>You will lead forward-looking R&amp;D efforts, explore and prototype breakthrough ideas, and set the technical direction as this company scales across the MENA region. Working directly with the founders, you’ll have a primary voice in shaping not only <i>what </i>we build, but <i>how </i>we think.</p><br><p><br><b>Discover the Role</b></p><br><ul><li><p>Lead Technical Innovation: Identify whitespace opportunities, challenge legacy solutions, and architect future-ready systems for credit intelligence.</p><br></li><li><p>Prototype &amp; Build: Translate abstract concepts into high-impact prototypes and production-grade systems using AI and real-time data infrastructure.</p><br></li><li><p>Set Technical Direction: Own the architectural roadmap and strategic vision for R&amp;D initiatives with autonomy and clarity.</p><br></li><li><p>Push the Edge: Evaluate and apply emerging techniques in foundation models, causal inference, streaming analytics, and real-time ML.</p><br></li><li><p>Collaborate Across Disciplines: Partner with product, design, data science, and executive leadership to align technical innovation with strategic goals.</p><br></li><li><p>Mentor &amp; Elevate: Raise the bar for engineering and research excellence while building and scaling a high-performing team around you.</p><br></li></ul><p><br><b>Discover the Responsibilities </b></p><br><ul><li><p>You’ve led complex, high-impact engineering or AI projects at companies known for technical excellence (Big Tech, top-tier AI labs, or venture-backed startups).</p><br></li><li><p>You possess a strong grasp of the math, the models, and their practical application to real-world systems.</p><br></li><li><p>You are an active individual contributor who codes daily, prototypes rapidly, and understands systems at scale.</p><br></li><li><p>You combine vision with execution—you don’t just rebuild what’s been done, you imagine what’s possible.</p><br></li><li><p>You’ve worked across the full stack—from data infrastructure and backend systems to deploying ML models in production.</p><br></li><li><p>You thrive in ambiguous environments and operate with a bias toward bold action and continuous iteration.</p><br></li></ul><p><br><b>Discover the Requirements</b></p><br><ul><li><p>8+ years of engineering experience, with a strong track record of leading innovation in AI or data-intensive products.</p><br></li><li><p>Hands-on expertise with Python, distributed data systems (e.g., Spark, Kafka, Snowflake, Databricks), and ML frameworks (e.g., PyTorch, TensorFlow).</p><br></li><li><p>Proven experience architecting, deploying, and scaling production-grade systems.</p><br></li><li><p>Demonstrated ability to think creatively, challenge conventions, and lead R&amp;D from 0 to 1.</p><br></li><li><p>Deep understanding of the current AI research landscape and how to translate cutting-edge research into working software.</p><br></li></ul><br> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>ADIC is seeking a Quantitative Researcher to join the Strategy team.<br> The successful candidate will play a key role in supporting ADIC's investment process through quantitative research, model development, and AI-driven solutions.<br> This role offers the opportunity to contribute to investment decision-making across both public and private markets by developing systematic signals, valuation models, and portfolio construction tools.<br> The successful candidate will work closely with investment professionals across the Strategy team to enhance quantitative capabilities and support the development of innovative investment solutions.<br> Key Responsibilities Generate and test alpha signals and factor ideas across asset classes using statistical and machine learning techniques.<br> Conduct quantitative research and develop valuation models across both public and private markets.<br> Design, backtest, and evaluate quantitative models, systematic strategies, and portfolio construction frameworks.<br> Build and enhance risk models, performance attribution frameworks, and investment analytics.<br> Develop end-to-end quantitative tools and applications to support investment workflows, from data ingestion through to deployment.<br> Partner with investment professionals across the Strategy team to deliver research, model specifications, and quantitative insights.<br> Drive AI and machine learning initiatives, identifying opportunities to enhance research and investment processes.<br> RequirementsExperience Minimum 5 years of relevant experience in quantitative research, systematic investing, portfolio construction, asset allocation, or investment strategy.<br> Experience conducting quantitative research across public markets, with exposure to private markets considered advantageous.<br> Proven experience designing, backtesting, and implementing quantitative models or systematic investment strategies.<br> Experience applying AI and machine learning techniques to financial datasets and developing production-ready analytical tools.<br> Education Bachelor's degree in Finance, Mathematics, Engineering, Computer Science, Statistics, Physics, or another quantitative discipline.<br> Master's degree or PhD is considered a strong advantage.<br> Technical Skills &amp; Knowledge Strong programming skills in Python or another object-oriented language, with experience developing production-quality code.<br> Good understanding of quantitative modelling, time-series analysis, factor models, and portfolio optimisation.<br> Experience with machine learning frameworks such as scikit-learn, TensorFlow or PyTorch.<br> Knowledge of SQL, cloud platforms, and Git-based development practices.<br> Strong understanding of financial markets, including equities, fixed income, private markets, and their application to portfolio management and asset allocation.<br> Excellent analytical and communication skills, with the ability to present complex quantitative findings to investment stakeholders.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><ol><li><p>Data Analysis &amp; Insight Generation</p></li></ol><p>Analyze large datasets to identify trends and generate actionable insights for decision making and operational efficiency.</p><p>Communicate findings through visualizations and clear storytelling.</p><ol><li><p>Machine Learning &amp; Model Development</p></li></ol><p>Develop and deploy machine learning models for use cases such as forecasting, optimization, and risk analysis.</p><p>Apply statistical and machine learning techniques to solve complex business problems.</p><ol><li><p>Data Preparation &amp; Feature Engineering</p></li></ol><p>Prepare data through extraction, cleaning, preprocessing, and feature engineering.</p><p>Ensure data quality and integrity for reliable model performance.</p><ol><li><p>Model Deployment &amp; Monitoring</p></li></ol><p>Deploy models into production and monitor performance for drift or degradation.</p><p>Continuously improve models based on feedback and new data.</p><ol><li><p>Collaboration &amp; Stakeholder Engagement</p></li></ol><p>Work with business teams to translate requirements into analytical solutions.</p><p>Collaborate with Data Engineering and Data Governance teams for end to end delivery.</p><ol><li><p>AI Governance</p></li></ol><p>Ensure all AI and machine learning activities comply with governance, privacy, and regulatory requirements by following defined lifecycle processes, risk classification, and using approved governed data.</p><p>Implement responsible and secure AI practices, including bias and fairness checks, explainability, model documentation, auditability, and collaboration with Cybersecurity and Data Governance teams.</p></div></section><section><p class="heading">Desired Candidate Profile</p><p class="paragraph"></p><ul><li><p>Minimum Qualifications</p><ul><li><p>Bachelor's or Master's Degree in Data Science, Artificial Intelligence (AI), Statistics, Computer Science, or a related field.</p></li></ul><p>Minimum Experience</p><ul><li><p>Minimum <strong>5+ years</strong> of experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.</p></li><li><p>Proven experience developing, deploying, and maintaining machine learning models in production environments.</p></li><li><p>Experience working with large-scale datasets and cloud-based analytics platforms is preferred.</p></li></ul><p>Skills &amp; Competencies</p><ul><li><p>Strong programming skills in <strong>Python</strong> (preferred) or <strong>R</strong>.</p></li><li><p>Experience with large-scale data processing using <strong>SQL</strong>, <strong>Spark</strong>, or similar technologies.</p></li><li><p>Hands-on experience with machine learning frameworks such as <strong>TensorFlow</strong>, <strong>PyTorch</strong>, and <strong>Scikit-learn</strong>.</p></li><li><p>Experience in end-to-end machine learning model development, validation, deployment, and optimization.</p></li><li><p>Knowledge of <strong>MLOps</strong> practices, including CI/CD pipelines, model monitoring, version control, and automated retraining.</p></li><li><p>Understanding of data engineering concepts, data pipelines, and cloud data platforms, preferably <strong>Microsoft Azure</strong> and <strong>Databricks</strong>.</p></li><li><p>Strong knowledge of statistical analysis, predictive modeling, feature engineering, and data preprocessing techniques.</p></li><li><p>Experience using data visualization tools such as <strong>Power BI</strong> and <strong>Tableau</strong> to communicate analytical insights.</p></li><li><p>Understanding of data governance, data quality management, privacy regulations, and AI governance principles.</p></li><li><p>Strong analytical thinking, problem-solving, and critical reasoning skills.</p></li><li><p>Excellent communication, presentation, and stakeholder management skills.</p></li><li><p>Ability to work collaboratively with cross-functional teams in an agile environment.</p></li><li><p>Strong organizational skills with the ability to manage multiple priorities and deliver high-quality outcomes within deadlines.</p></li></ul></li></ul><p></p></section>

<strong>About Us<br><br></strong>At Dyson, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.<br><br>You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.<br><br><strong>About The Role<br><br></strong>We are looking for a specialized Senior Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.<br><br><strong>Key Responsibilities<br><br></strong><ul><li>Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.</li><li>Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.</li><li>Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.</li><li>Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.</li><li>Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.</li><li>Set up and maintain robust data preparation infrastructure—optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.</li><li>Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.</li><li>Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.<br><br><br></li></ul><strong>About you<br><br></strong><ul><li>At least 5+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.</li><li>Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).</li><li>Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).</li><li>Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).</li><li>Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.</li><li>Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.</li><li>Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.</li><li>Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).</li><li>Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).</li><li>Strong background in feature engineering, data analysis, and data visualization—comfortable using tools like Jupyter, Tableau, or Power BI.</li><li>Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.</li><li>Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.</li><li>Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.<br><br><br></li></ul>Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.

<strong>About Us<br><br></strong>At Dyson, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.<br><br>You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.<br><br><strong>About The Role<br><br></strong>We are looking for a specialized Lead Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.<br><br><strong>Key Responsibilities<br><br></strong><ul><li>Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.</li><li>Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.</li><li>Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.</li><li>Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.</li><li>Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.</li><li>Set up and maintain robust data preparation infrastructure—optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.</li><li>Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.</li><li>Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.<br><br><br></li></ul><strong>About you<br><br></strong><ul><li>At least 8+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.</li><li>Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).</li><li>Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).</li><li>Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).</li><li>Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.</li><li>Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.</li><li>Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.</li><li>Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).</li><li>Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).</li><li>Strong background in feature engineering, data analysis, and data visualization—comfortable using tools like Jupyter, Tableau, or Power BI.</li><li>Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.</li><li>Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.</li><li>Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.<br><br><br></li></ul>Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.

<strong>About Us<br><br></strong>At Dyson, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.<br><br>You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.<br><br><strong>About The Role<br><br></strong>We are looking for a specialized Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.<br><br><strong>Key Responsibilities<br><br></strong><ul><li>Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.</li><li>Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.</li><li>Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.</li><li>Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.</li><li>Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.</li><li>Set up and maintain robust data preparation infrastructure—optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.</li><li>Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You’ll help transform raw data into actionable insight, supporting both research and deployment.</li><li>Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.<br><br><br></li></ul><strong>About you<br><br></strong><ul><li>At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.</li><li>Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).</li><li>Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).</li><li>Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).</li><li>Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.</li><li>Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.</li><li>Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.</li><li>Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).</li><li>Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).</li><li>Strong background in feature engineering, data analysis, and data visualization—comfortable using tools like Jupyter, Tableau, or Power BI.</li><li>Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.</li><li>Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.</li><li>Bachelor’s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.<br><br><br></li></ul>Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.