Job Title: AI/ML Architect Location: Mumbai (with potential travel to multiple global office locations including the USA, UAE, Saudi Arabia, and Singapore) Experience Level: 10- 12 years (6–8 years in engineering/data science roles and at least 2-3+ years in a dedicated AI/ML architecture role)
About the Role We are seeking a highly experienced Enterprise Data and AI Architect to design, implement, and scale enterprise-grade AI solutions across industries such as BFSI, Retail, Manufacturing, and Healthcare. This is a strategic, hands-on leadership role designed to transform legacy data silos into modern Data Mesh or Data Lakehouse architectures. The ideal candidate will bridge the gap between "Proof of Concept" (PoC) and full-scale enterprise production by deploying cutting-edge Machine Learning, Generative AI, and Agentic AI systems. You will collaborate closely with product teams, pre-sales, and executive stakeholders to translate complex business problems into secure, scalable, and autonomous AI solutions.
Key Responsibilities AI Strategy & Solution Architecture Define and evolve the enterprise AI architecture to align with business, data, and technology strategies. Ability to translate complex business challenges into scalable AI-driven technical architectures Design end-to-end AI and ML architectures spanning data ingestion, pre-processing, model training, deployment, and monitoring. Architect scalable, secure, and compliant AI deployment models (Cloud and Hybrid) and intelligent automation solutions. Develop reference architectures and reusable patterns for generative AI, Agentic AI, predictive models, conversational systems, and intelligent automation. Evaluate and select the appropriate AI technologies, frameworks, cloud services, LLM orchestration frameworks, vector databases, and tooling.
Generative AI, LLM & Agentic AI Implementation Architect and build Gen AI solutions and LLM-powered applications using RAG, fine-tuning, and prompt engineering. Design and implement Agentic AI systems capable of autonomous decision-making, multi-step task execution, and function-calling in production. Establish custom orchestration frameworks for multi-agent collaboration, memory management, planning, evaluation harnesses, and tool invocation. Work across major AI foundries and APIs, including Azure Open AI, AWS Bedrock, Google Vertex AI, Anthropic Claude SDK, and Open AI APIs. Design RAG pipelines using vector databases and meticulously tune embeddings to ensure high retrieval quality. Optimize prompts, implement response validation mechanisms, and systematically reduce hallucination risks. Writing clean and maintainable code, Agile methodologies, Git version control, and testing methodologies
Machine Learning Model Development Design, develop, deploy, and optimize advanced ML algorithms, leveraging techniques in supervised/unsupervised learning, deep learning, reinforcement learning, NLP, and computer vision. Select appropriate mathematical and statistical modeling techniques, handling comprehensive data preparation, wrangling, feature engineering, and dimensionality reduction. Train and deploy ML models utilizing distributed computing frameworks and tools like PyTorch, Tensor Flow, MLflow, Sage Maker, Azure ML, or Kubeflow.
Data Engineering, Cloud Governance & Integration Overhaul legacy data silos into modern Data Lakehouse or Data Mesh architectures to support predictive analytics and real-time business intelligence. Partner with data architects to define robust data pipelines, data governance, feature stores, and complex ETL/ELT processes. Design secure and performant integration patterns to connect AI agents and models dynamically with ERP, CRM, workflow engines, APIs, and microservices.
MLOps, LLMOps & Production Operations Define and implement comprehensive MLOps/LLMOps standards, including CI/CD pipelines, model versioning, lifecycle observability, and rollback processes. Establish rigorous monitoring frameworks spanning model drift detection, performance tracking, telemetry, and establishing KPIs for production AI systems. Monitor autonomous agent performance, define evaluation metrics (task success rate, reasoning validation, error handling), and track decision audit trails. Drive continuous improvement of model performance, computational efficiency, and operational cost.
AI Governance, Security & Ethics Establish enterprise AI governance standards, ensuring compliance with data privacy policies (PII handling), role-based access, and IT security protocols (e.g., AWS IAM, Guard Duty). Implement "Responsible AI" guardrails tailored for regulated industries, including fairness checks, bias detection, and safety filters. Design human-in-the-loop mechanisms for agent oversight, define autonomy thresholds, and establish escalation protocols. Ensure complete traceability, automation audit trails, and explainability in AI-driven decisions.
Technical Leadership, Collaboration & Research Partner with business, product, engineering, and compliance teams to translate complex business problems into viable AI/ML approaches. Create and deliver high-level technical presentations to executive stakeholders using data visualization tools to communicate complex strategies and business impact. Provide technical guidance, architectural oversight, and mentorship to junior engineers.
Required Skills & Qualifications
Technical Core & Programming:Mandatory: Strong programming proficiency in Python & Libraries such as Pandas, Num Py, and Scikit-learn Solid mathematical & Statistical foundation covering linear algebra, calculus, probability theory, and statistics. Database: SQL for relational databases and NoSQL for unstructured data handling Developing scalable microservices via RESTful APIs, Fast API, g RPC Server Machine Learning & Deep Learning:Proficiency with ML frameworks such as Tensor Flow, PyTorch, and Scikit-learn. Hands-on experience implementing algorithms like decision trees, random forests, SVMs, and neural networks. Expertise in deep learning architectures (CNN, RNN, GAN). Expertise in Natural Language Processing (NLP) and recommendation systems Hands-on experience with Multimodal NLP implementation, such as Computer Vision (Open CV) and Speech-to-Text Generative AI, LLMs & Agentic Orchestration (Must Have):Extensive experience interacting/integration with Foundation LLM Models ( such as GPT, Claude, Gemini, LLaMA, Mistral, and Deep Seek) Proven ability in Model Fine-Tuning & Customization Experience with LLM frameworks like Lang Chain and Llama Index. Proven expertise in designing multi-agent orchestration using Lang Graph, Crew AI, Auto Gen, or equivalent tools. Hands-on experience with AI foundries and Copilots: Open AI, Azure AI Foundry, MSFT Copilot Studio Agent Builder, AWS Bedrock, Vertex AI, Anthropic Claude, and low-code/no-code platforms Data Platforms, Vector Databases & Infrastructure:Mandatory Skills: Data Warehousing. Deep expertise in the AWS Ecosystem (S3, Sage Maker, Redshift, Glue, Bedrock, EKS) alongside Azure or GCP exposure. Experience with large-scale data processing tools like Snowflake, Databricks, or Apache Spark. Implementation of RAG using Vector Databases (Pinecone, FAISS, Chroma, Azure AI Search, etc). Moderate-level background in Infrastructure as Code (IaC) via Terraform or AWS Cloud Formation. Proficiency in architectural patterns (microservices, event-driven, API-first design) and Database knowledge (SQL/NoSQL) MLOps & Observability Tools:Experience across the ML/LLM lifecycle using MLflow, Kubernetes, and evaluation/observability tooling. Proficiency in data visualization tools to analyze and present model insights
Experience & Educational Requirements Education:Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or a related technical field.
Minimum Experience Requirements:10–12 years of overall professional experience. Minimum 4 years of experience in application development, engineering, or solution delivery roles. Minimum 2+ years dedicated to AI architecture roles. Minimum 3 years of hands-on experience in AI/ML engineering, data science, or AI solution architecture. Minimum 2 years designing and implementing scaled Agentic AI and Generative AI solutions/platforms in active operations.