Artificial Intelligence Expert

2 weeks ago

Thrissur, Kerala, India beBeeAI Full time ₹ 15,00,000 - ₹ 20,00,000
Advanced AI Research Scientist

As an advanced AI research scientist, you will work on the intersection of artificial intelligence and software development. This role combines deep, publishable AI research with hands-on system development.

You will bridge the gap between science and software by innovating on large language models, prompt systems, evaluation frameworks, and algorithm pipelines.

  • Tackle experiments in LLM training, fine-tuning, reinforcement learning, and new evaluation methodologies.
  • Build code-first prototypes to test hypotheses and iterate rapidly – refactoring them into production systems when experiments succeed.

Key Responsibilities:

Research Prototyping
  • Develop experimental prototypes rich enough for academic-quality write-ups.
  • Productize those ideas into robust, reliable software.
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: data pipelines, experiment sandbox, 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 fields (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.

Success Metrics:

  • Prototypes demonstrating clear scientific advances.
  • Papers submitted or draft-ready internal research docs.
  • Experimental pipelines and prototypes are production-ready within sprint cycles.
  • System impact and user adoption.

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