SeniorMLEngineer(GenAI,AWS)

## Responsibilities Technical Delivery (60%) - Design and implement end-to-end ML solutions from experimentation to production; - Build scalable ML pipelines and infrastructure; - Optimize model performance, efficiency, and reliability; - Write clean, maintainable, production-quality code; - Conduct rigorous experimentation and model evaluation; - Troubleshoot and resolve complex technical challenges. Collaboration and Contribution (25%); - Mentor junior and mid-level ML engineers; - Conduct code reviews and provide constructive feedback; - Share knowledge through documentation, presentations, and workshops; - Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); - Contribute to internal ML practice development. Innovation and Growth (15%) - Stay current with ML research and emerging technologies; - Propose improvements to existing solutions and processes; - Contribute to the development of reusable ML accelerators; - Participate in technical discussions and architectural decisions. ## Requirements Machine Learning Core - ML Fundamentals: supervised, unsupervised, and reinforcement learning; - Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation; - ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks; - Deep Learning: CNNs, RNNs, Transformers. LLMs and Generative AI - LLM Applications: Experience building production LLM-based applications; - Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies; - RAG Systems: Experience building retrieval-augmented generation architectures; - Vector Databases: Familiarity with embedding models and vector search; - LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs. Data and Programming - Python: Advanced proficiency in Python for ML applications; - Data Manipulation: Expert with pandas, numpy, and data processing libraries; - SQL: Ability to work with structured data a