Principal Data Scientist

2 days ago

Bengaluru, Karnataka, India IITMatrix Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Position Details

  • Profile Name:
    Principal Data Scientist – GenAI

Qualification & Eligibility

  • Bachelor's or higher degree in a quantitative discipline (Computer Science, Statistics, Engineering, Applied Mathematics).

Work Experience

  • Minimum
    8+ years of experience
    .
  • Startup experience preferred; Edtech work experience is a plus.

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 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
    (truthfulness, reasoning quality, safety, alignment).
  • Hands-on expertise in
    fine-tuning approaches
    : instruction tuning, LoRA, PEFT, model merging, distillation, prompt-tuning.
  • Strong background in
    representation learning, transformers, IR/retrieval models, and embeddings
    .
  • Proficiency in
    Python, PyTorch/TensorFlow
    , and production ML pipelines.
  • Experience with
    MLOps practices
    : model serving, monitoring, observability for LLM systems.

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