Role description
• Design develop and deploy MLAI models for real time and batch use cases including model experimentation training and evaluation
• Build and optimize inference pipelines and integrate ML capabilities into applications services in partnership with product and engineering teams
• Develop and maintain data pipelines for model training validation and continuous improvement retraining continual learning
• Monitor model performance in production quality drift bias hallucinations where applicable and drive improvements in reliability and robustness
• Establish engineering best practices for ML delivery reproducibility versioning testing documentation and benchmarking experimentation
• Contribute to solution architecture decisions for ML systems data compute deployment patterns and operational controls
• Mentor junior engineers and lead technical reviews for ML code pipelines and deployment implementations 7 -12 years of experience in software engineering data engineering ML engineering with significant hands on time delivering ML solutions
• Strong proficiency in Python and MLDL libraries such as PyTorch TensorFlow and familiarity with modern model ecosystems eg Hugging Face
• Solid understanding of ML fundamentals feature engineering model selection evaluation metrics overfitting cross validation and deep learning concepts neural nets transformers where relevant
• Experience with model deployment approaches tools eg model serving ONNX Torch Serve Triton or equivalent
• Strong engineering practices clean code debugging performance optimization API integration and collaboration in cross functional teams
• Experience with MLOps GenAI Ops tooling such as ML flow containerization Docker and cloud platforms AWS, Azure, GCP for scalable ML delivery Experience with LLMs Generative AI fine tuning prompt engineering evaluation and production patterns
• Familiarity with RAG and vector databases plus responsible ethical AI practices and governance
• Experience building automated benchmarking AB testing and monitoring frameworks for ML systems
• Contributions to open source publications patents or strong internal innovation track record Strong ownership and ability to lead quality outcomes end-to-end
• Clear communication and stakeholder management
• Mentoring mindset and collaboration across QA Dev and DevOps teams
