AI Researcher

23 hours ago

Vizag, Andhra Pradesh, India beBeeArtificialintelligence Full time ₹ 1,80,00,000 - ₹ 2,40,00,000
Job Description

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

You are a bridge between science and software. 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.

Main 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.
  • Develop and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  • Optimize for performance, resource efficiency, and interpretability.
  • 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.
  • Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  • Enable internal teams to run reproducible experiments at scale.
  • 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 Computer Science, Machine Learning, or related field (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
Success Metrics
  • Prototypes demonstrating clear scientific advances (e.g. better evaluation metrics or prompt robustness). Papers submitted or draft-ready internal research docs.
  • Experimental pipelines and prototypes are production-ready within sprint cycles; clean codebases, smooth deployments, few bugs.
  • R&D code drives real product features—high user adoption, improved reliability or performance.

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