- Lead the roadmap for Generative AI (GenAI) solutions across product and platform teams.

- Architect and optimise RAG pipelines (retrieval, embeddings, hybrid search, re-ranking, caching, latency-cost tradeoffs).

- Establish robust evaluation frameworks (automatic + human-in-the-loop) to measure LLM outputs on factuality, reasoning, hallucinations, coverage, and safety.

- Lead research and applied innovation around foundation model fine-tuning (instruction tuning, LoRA, adapters, PEFT, multi-task fine-tuning).

- Mentor senior scientists/engineers, setting standards for model development, experimentation, and research rigour.

- Partner with Product, Engineering, and Business stakeholders to deliver AI systems with measurable business impact.

Skill Sets Required :

- Deep understanding of foundational LLMs (architectures, tokenization, pre-training vs fine-tuning, context window optimization).

- Strong experience with RAG systems (vector DBs, hybrid retrieval, embeddings, ranking models).

- Proven ability to design and implement evaluation pipelines.