Senior AI Development Lead

2 weeks ago

Pune, Maharashtra, India beBeeInnovative Full time ₹ 1,20,00,000 - ₹ 2,40,00,000
AI Researcher

This is an exciting opportunity for a talented researcher to blend deep AI research with hands-on system development, ensuring the creation of groundbreaking AI capabilities end-to-end.

You will be a bridge between science and software. Your task is to 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.

Key Responsibilities:
  • Research Prototyping: Tackle experiments in LLM training, LLM agent architectures, fine tuning, reinforcement learning, and new evaluation methodologies.
  • Build Code-First Prototypes: Build code-first prototypes to test hypotheses and iterate rapidly—refactor them into production systems when experiments succeed.
Model & Architecture Innovation
  • Develop and Benchmark Improvements: Develop and benchmark improvements in LLM architecture, fine-tuning workflows, and generation control.
  • Optimize for Performance: Optimize for performance, resource efficiency, and interpretability.
Production Engineering
  • Own Infrastructure: Own infrastructure: from data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
  • Treat Experiments as Software: Treat experiments as software: automated tests, clear versioning, reproducibility, and documentation.
Scientific Experiment Tooling
  • Create Frameworks: Create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards.
  • Enable Internal Teams: Enable internal teams to run reproducible experiments at scale.
Knowledge Sharing & Publication
  • Write Up Findings: Write up interesting findings—internal memos, tech blogs, or scholarship-worthy results.
  • Participate in Critique Sessions: Participate in code and science critique sessions with peers.
Qualifications:

Master's or Ph.D. in CS, ML, or related field (or equivalent experience)

  • Deep Familiarity with LLMs: Deep familiarity with large language models, fine-tuning, reinforcement learning.
  • Expertise in Python & Frameworks: Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs.
  • Strong Software Engineering Intuition: Strong software engineering intuition: modular design, CI/CD, containerization, automated testing.
  • Demonstrated Track Record: Demonstrated track record in both research and production settings (e.g., prototypes published internally or externally).
  • Bonus Skills: 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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