AI Research Engineer

2 days ago

Mysore, Karnataka, India beBeeArtificial Full time ₹ 18,00,000 - ₹ 20,25,000
Job Description

This role combines cutting-edge AI research with hands-on system development. You will conduct research, build, validate, and deploy groundbreaking AI capabilities end-to-end.

You are a bridge between science and software. You will innovate on LLMs, prompt systems, evaluation frameworks, and algorithm pipelines – bringing them to production-grade systems that power real users.

Key Responsibilities
  1. Tackle experiments in LLM training, LLM agent architectures, fine tuning, RL, and new evaluation methodologies.
  2. Build code-first prototypes to test hypotheses and iterate rapidly – refactoring them into production systems when experiments succeed.
Model & Architecture Innovation
  1. Develop and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  2. Optimize for performance, resource efficiency, and interpretability.
Production Engineering
  1. Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
  2. Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.
Scientific Experiment Tooling
  1. Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  2. Enable internal teams to run reproducible experiments at scale.
Knowledge Sharing & Publication
  1. Write up interesting findings – internal memos, tech blogs, or scholarship-worthy results.
  2. Participate in code and science critique sessions with peers.
Requirements
  • Master's or Ph.D. in CS, ML, or related (or equivalent experience)
  • Deep familiarity with LLMs, fine-tuning, reinforcement learning
  • Expert 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
How We Measure Success
  • Research Innovation
  • Prototypes demonstrating clear scientific advances (e.g. better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.
  • Engineering Quality & Velocity
  • Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.
  • System Impact & Use
  • R&D code drives real product features – high user adoption, improved reliability or performance.

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