Senior Data Scientist

Description

• Debug and resolve retrieval quality issues, latency bottlenecks, and cost optimization challenges
• Optimize vector databases for enterprise-scale semantic search, context management, chat history, and source attribution
• Research and develop multi-agentic systems for production-grade applications that handle large-scale documents and big data
• Work in AWS cloud platform for deployment – handle concurrent requests and scale
• Integrate and optimize LLMs via AWS Bedrock, or Azure with hallucination mitigation
• Implement advanced retrieval strategies: hybrid search, re-ranking, and custom evaluation frameworks
• Vector database production implementation: indexing, retrieval optimization, performance tuning
• Evaluation & metrics for RAG systems & chatbots
• Build document processing pipelines with intelligent chunking, metadata extraction, and multi-format handling

Requirements

• 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)
• Advanced retrieval techniques: hybrid search (semantic + keyword), multi-stage retrieval
• Knowledge of deployment, monitoring, ethics, and governance principles; RESTful API development with FastAPI including error handling
• Built and implemented enterprise-level GenAI chatbots capable of natural-language search over extensive document repositories and databases.
• Embeddings and semantic search: sentence-transformers, similarity search, distance metrics implementation
• Production LLM integration / hosting: context management, token optimization, streaming responses, citation systems

Benefits

• Opportunity to mentor and shape GenAI practices across the organization

Training + Development

Interview process

Visa Sponsorship

Security clearance