Senior AI Innovation Lead

1 day ago

Aurangabad, Maharashtra, India beBeeAIResearch Full time ₹ 2,00,00,000 - ₹ 2,50,00,000

We are looking for a highly skilled AI Researcher to join our team and drive innovation in the field of artificial intelligence. This role combines deep, publishable AI research with hands-on system development, ensuring you can conduct research, build, validate, and ship groundbreaking AI capabilities end-to-end.

You will innovate on large language models (LLMs), prompt systems, evaluation frameworks, and algorithm pipelines—and bring them to production-grade systems that power real users. Expect to author experimental prototypes rich enough for academic-quality write-ups, then productize those same ideas into robust, reliable software.

Key Responsibilities:
  • Tackle experiments in LLM training, LLM agent architectures, fine-tuning, reinforcement learning, and new evaluation methodologies.
  • Build code-first prototypes to test hypotheses and iterate rapidly—refactor 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 and experiment sandbox 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.

To be successful in this role, you should have:

  • A Master's or Ph.D. in Computer Science, Machine Learning, 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 (e.g., prototypes published internally or externally)
  • Bonus: familiarity with Kubernetes, JAX compiler stacks (e.g. Flax, Alpa), Hugging Face Accelerate, or MLOps toolchains

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