Primary Responsibilities:

Built and implemented enterprise-level GenAI chatbots capable of natural-language search over extensive document repositories and databases.
Research and develop multi-agentic systems for production-grade applications that handle large-scale documents and big data
Optimize vector databases for enterprise-scale semantic search, context management, chat history, and source attribution
Build document processing pipelines with intelligent chunking, metadata extraction, and multi-format handling
Implement advanced retrieval strategies: hybrid search, re-ranking, and custom evaluation frameworks
Work in AWS cloud platform for deployment handle concurrent requests and scale
Integrate and optimize LLMs via AWS Bedrock, or Azure with hallucination mitigation
Debug and resolve retrieval quality issues, latency bottlenecks, and cost optimization challenges
Opportunity to mentor and shape GenAI practices across the organization
Individual Contributor reporting to Engineering Manager

Skills Required:

Experience building GenAI applications from scratch including custom chunking strategies, metadata extraction, multi-format document processing pipelines, indexing, context management, memory management, fine-tuning techniques, model compression techniques, etc. (Must be able to demonstrate through technical justification and code)
Vector database production implementation: indexing, retrieval optimization, performance tuning
Embeddings and semantic search: sentence-transformers, similarity search, distance metrics implementation
Advanced retrieval techniques: hybrid search (semantic + keyword), multi-stage retrieval
Production LLM integration / hosting: context management, token optimization, streaming responses, citation systems
Evaluation & metrics for RAG systems & chatbots
Knowledge of deployment, monitoring, ethics, and governance principles; RESTful API development with FastAPI including error handling

Senior Data Scientist