- End-to-End Solution Delivery : Take full ownership of the end-to-end delivery of both traditional Machine Learning (ML) and Generative AI (GenAI) use cases, ensuring successful deployment and measurable business impact.

- GenAI Architecture & Development : Architect advanced Retrieval Augmented Generation (RAG) pipelines and build robust, enterprise-scale GenAI accelerators.

- Collaboration & Strategy : Collaborate closely with solution architects, presales teams, and business stakeholders to understand requirements, define project scope, and align data science initiatives with overall business objectives.

- Code Quality & Governance : Ensure the development of highly modular, testable, and maintainable code.

- Implement and enforce strong code governance, best practices, and documentation standards.

- Mentorship & Leadership : Provide technical leadership and mentorship to junior data scientists, guiding them in their project work, skill development, and career growth.

Required Skills :

Generative AI (GenAI) :

- Strong practical experience with LangChain and LangGraph.

- Expertise in embedding techniques and advanced prompt engineering.

Classical Machine Learning :

- Proficiency with algorithms such as XGBoost, Random Forest, and various time-series forecasting models.

Programming & Frameworks :

- In-depth knowledge of the Python ML stack (e.g., NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch).

- Hands-on experience with FastAPI for building robust APIs.

Cloud & MLOps :

- Strong understanding of security best practices in cloud environments.

- Expertise in cloud optimization for ML workloads (e.g., cost, performance).