- Architect and implement scalable AI solutions on Azure, leveraging services like Azure Machine Learning, Azure OpenAI, and Azure Databricks.
- Lead the design and development of complex AI models, including LLMs, computer vision, and natural language processing, tailored for specific business needs.
- Drive the integration of AI capabilities into existing enterprise applications and workflows, ensuring seamless deployment and adoption.
- Optimize AI models for performance, cost-efficiency, and reliability within the Azure cloud environment.
- Mentor junior developers and data scientists, fostering a culture of innovation and best practices in AI development.
- Collaborate with product managers and stakeholders to translate business requirements into robust AI solutions.
- Stay abreast of the latest advancements in AI research and Azure AI services, proactively identifying opportunities for innovation.
- Develop and maintain CI/CD pipelines for AI models, ensuring automated testing, deployment, and monitoring.
- Troubleshoot and resolve complex technical issues related to AI model performance and Azure infrastructure.
- Contribute to the development of reusable AI components and frameworks to accelerate future project delivery.
Desired Candidate Profile
8+ years of professional software development experience, with at least 3 years focused on AI/ML solutions.
Proven expertise in developing and deploying machine learning models using Python and relevant libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with Azure AI services, including Azure Machine Learning, Azure OpenAI, Azure Cognitive Services, and Azure Databricks.
Strong understanding of MLOps principles and tools for model lifecycle management.
Excellent problem-solving skills and the ability to architect complex, scalable AI systems.
Proficiency in cloud-native development practices and containerization technologies (e.g., Docker, Kubernetes).
Exceptional communication and collaboration skills, with the ability to explain technical concepts to non-technical audiences.
Experience working in a fast-paced, agile development environment.
Familiarity with data engineering principles and working with large datasets.