Key Responsibilities:

Lead end-to-end analytics lifecycle: problem framing, data gathering, feature engineering, modeling, validation, and stakeholder communication. Design and build LLM-powered and agent-based solutions (e.g., RAG pipelines, workflow automation, decision-support copilots). Own productionization and scaling of models and AI systems on cloud platforms, ensuring performance, reliability, and cost efficiency. Develop and maintain ML and statistical models alongside LLM pipelines, integrating structured and unstructured data. Establish evaluation and monitoring frameworks for both ML models and LLM outputs (accuracy, drift, hallucination, business KPIs). Collaborate with business and engineering teams to translate requirements into scalable, production-ready solutions.



Ensure data quality, governance, and responsible AI practices across all implementations. Manage and support a team or set of contributor Provide guidance, feedback, and ensure alignment with delivery goals Drive accountability and ownership across team members Hands-on experience with LLMs/NLP systems, including prompt engineering, RAG, or fine-tuning approaches. The candidate should have experience in putting LLM based applications in production. Experience with agentic frameworks / orchestration tools (e.g., LangChain, LlamaIndex) and vector databases.

📌 Data Science Manager (Karnataka)
🏢 Quess
📍 Karnataka