Innovation Lead for Large Language Models

5 days ago

Jaipur, Rajasthan, India beBeeInnovator Full time ₹ 1,70,00,000 - ₹ 2,60,00,000
Senior AI Researcher

This role combines cutting-edge AI research with hands-on system development to drive innovation in large language models, prompt systems, and evaluation frameworks.

You will be responsible for bridging the gap between scientific research and software development, driving the creation of production-grade systems that power real users.

Key Responsibilities:
  • Conduct experiments in LLM training, fine-tuning, reinforcement learning, and new evaluation methodologies.
  • Build prototypes to test hypotheses and iterate rapidly, refactoring them into production systems when experiments succeed.
Model & Architecture Innovation:
  • Develop and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  • Optimize for performance, resource efficiency, and interpretability.
Production Engineering:
  • Own infrastructure from data pipelines to model serving, batch/online inference, logging, and CI/CD.
  • Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.
Scientific Experiment Tooling:
  • Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  • Enable internal teams to run reproducible experiments at scale.
Knowledge Sharing & Publication:
  • Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
  • Participate in code and science critique sessions with peers.
Requirements:
  • Master's or Ph.D. in CS, ML, or related field (or equivalent experience).
  • Deep familiarity with LLMs, fine-tuning, reinforcement learning.
  • Expertise in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs.
  • Strong software engineering intuition: modular design, CI/CD, containerization, automated testing.
  • Demonstrated track record in both research and production settings.
Measuring Success:
  • Research innovation through prototypes demonstrating clear scientific advances.
  • Engineering quality and velocity through clean codebases and smooth deployments.
  • System impact and use through R&D code driving real product features.

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