Key Responsibilities Solution Design and Delivery Translate business problems into Gen AI architectures (LLM, RAG, agentic patterns, multimodal). Build end-to-end prototypes and evolve them into reliable, secure production services. LLM Development Select and integrate foundation models (hosted APIs or open-source). Implement fine-tuning and parameter-efficient methods (e.g., LoRA/QLoRA/PEFT) where needed. Engineer prompts/system messages, tools/functions, and memory strategies. Retrieval and Data Implement RAG pipelines: chunking, embeddings, retrieval, re-ranking, and filtering. Work with vector databases and document stores; design data quality checks. Evaluation and Safety Define automated and human-in-the-loop evaluation for accuracy, toxicity, bias, and hallucinations. Implement guardrails, content filters, and policy enforcement. MLOps and Platform Package and deploy services with CI/CD, containerization, and IaC as applicable. Optimize inference for latency, throughput, and cost; monitor with observability tooling. Security, Privacy, and Compliance Handle PII securely; align with data governance, regulatory, and licensing constraints. Collaboration Partner with product, domain SMEs, data engineering, and SRE to deliver measurable outcomes. Document designs, decisions, and runbooks; share best practices.