Design, develop, and deploy LLM-powered applications for enterprise use cases

Build and optimize RAG pipelines, including:

Vector databases and embeddings

Document ingestion, chunking, indexing, and retrieval strategies

Implement and evaluate fine-tuning approaches (SFT, LoRA, adapters, PEFT techniques)

Work with enterprise LLM platforms ensuring:

Data security, privacy, and compliance

Token efficiency, latency optimization, and cost control

Integrate MCP (Model Context Protocol) or equivalent approaches to manage model context, tool usage, and structured prompts

Develop robust prompt engineering and prompt orchestration strategies

Design scalable APIs and microservices to serve GenAI workloads

Evaluate open-source and commercial LLMs (OpenAI, Azure OpenAI, Anthropic, open-source models, etc.)

Collaborate with MLOps and Platform teams on:

Model monitoring and observability

Versioning, rollback, and lifecycle management

Partner with business and product stakeholders to translate requirements into production-ready GenAI solutions

Data Scientist