Data Scientist

The project is focused on building production-grade GenAI solutionswith emphasis on:• RAG pipelines leveraging LLMs• Prompt engineering (system/tool prompts, function calling, versioning with evals)• Evaluation & observability (ground truth setup, confusion metrics, LLM-as-judge with human review, cost & latency monitoring)• Retrieval strategies & prompt patterns (context management, hallucination mitigation)• LangChain/LlamaIndex (or equivalent) proficiency• Cloud LLM providers (Azure OpenAI, AWS Bedrock, Vertex AI)• Workflow orchestration (Airflow, Dagster)• Security & privacy (PII handling, RBAC)• Python expertise with strong practical experienceSuccess criteria:• A production-ready prompt + RAG pipeline system with measurable quality lift (accuracy/confusion metrics) and reduced hallucinations, backed by a repeatable evaluation harness.