Senior AI Development Expert

2 days ago

Bellary, Karnataka, India beBeeArtificialintelligence Full time US$ 1,80,000 - US$ 2,50,000

Deliver cutting-edge advancements in Artificial Intelligence research by combining theoretical knowledge with hands-on system development expertise. As a Senior AI Researcher, you will conduct thorough research, build, validate and deploy innovative AI capabilities end-to-end.

You will be a bridge between scientific theory and software implementation. This role involves innovating on large language models, prompt systems, evaluation frameworks, and algorithm pipelines – and bringing 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, refining them into production systems when experiments are successful.
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:
  • Oversee infrastructure: from data pipelines and experiment sandboxes 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.
Requirements:
  • Master’s or Ph.D. in Computer Science, Machine Learning, or related field (or equivalent experience)
  • Deep familiarity with large language models, fine-tuning, reinforcement learning
  • Expertise in Python and 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
Measuring 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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