AI Development Leader

11 hours ago

Mount Abu, Rajasthan, India beBeeArtificial Full time ₹ 15,00,000 - ₹ 25,00,000

Deliver cutting-edge artificial intelligence solutions that push the boundaries of innovation.

Job Summary:

This role combines in-depth AI research with hands-on system development to ensure that you can conduct research, build, validate and deploy groundbreaking AI capabilities from start to finish.

Key Responsibilities:

- Conduct experiments on LLM training, agent architectures, fine-tuning, reinforcement learning, and new evaluation methodologies.

- Develop code-first prototypes to test hypotheses and iterate rapidly, then refactor 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:

- Manage 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:

- Document interesting findings: internal memos, tech blogs, or scholarship-worthy results.

- Participate in code and science critique sessions with peers.

Who You Are (Qualifications):

- Master's or Ph.D. in CS, ML, or related fields or equivalent experience.

- Deep understanding of LLMs, fine-tuning, reinforcement learning.

- Expert 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.

- Bonus: familiarity with Kubernetes, JAX compiler stacks, Hugging Face Accelerate, or MLOps toolchains.

How You'll Measure Success:

- Research Innovation: Prototypes demonstrating clear scientific advances.

- Engineering Quality & Velocity: Experimental pipelines and prototypes are production-ready within sprint cycles.

- System Impact & Use: R&D code drives real product features with high user adoption, improved reliability, or performance.

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