10 - 15 years
60 - 70 Lacs
Posted:7 hours ago|
Platform:
Work from Office
Full Time
Qualification & Eligibility:
Working Experience:
Roles & responsibilities:
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 fi ne-tuning (instruction tuning, LoRA, adapters, PEFT, multi-task fi ne-tuning).
Mentor senior scientists/engineers, setting standards for model development, experimentation, and research rigour.
Partner with Product, Engg, and Business stakeholders to deliver AI systems with measurable business impact.
Skill Sets:
Deep understanding of foundational LLMs (architectures, tokenization, pre-training vs fi ne-tuning, context window optimization).
Strong experience with RAG systems (vector DBs, hybrid retrieval, embeddings, ranking models). Proven ability to design and implement eval pipelines (truthfulness, reasoning quality, safety, alignment).
Hands-on expertise in fi ne-tuning approaches: instruction tuning, LoRA, PEFT, model merging, distillation, prompt-tuning.
Strong background in representation learning, transformers, IR/retrieval models, and embeddings.
Profi ciency in Python, PyTorch/TensorFlow, and production ML pipelines.
Experience with MLOps practices: model serving, monitoring, observability for LLM systems.
Physicswallah
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