AI Research Development Specialist

2 weeks ago

Alleppey, Kerala, India beBeeArtificialIntelligence Full time ₹ 15,00,000 - ₹ 25,00,000

Embark on a Groundbreaking AI Research Adventure

This role presents a unique opportunity to harmonize cutting-edge AI research with practical system development. As a pioneering researcher, you will design, build, validate, and deploy innovative AI capabilities from inception to production.

You will serve as a bridge between scientific innovation and software engineering, pushing the boundaries of Large Language Models (LLMs), prompt systems, evaluation frameworks, and algorithm pipelines—and bringing them to life in production-grade systems that power real users.

Research Prototyping
  • Drive experiments in LLM training, LLM agent architectures, fine-tuning, reinforcement learning, and new evaluation methodologies.
  • Develop code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.
Model & Architecture Innovation
  • Design 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
  • Document interesting findings—internal memos, tech blogs, or scholarship-worthy results.
  • Participate in code and science critique sessions with peers.

Key Qualifications:

  • 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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