Innovative AI Researcher

3 days ago

Anantapur, Andhra Pradesh, India beBeeArtificial Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Job Description

As a senior AI researcher, you will be bridging the gap between cutting-edge science and software development. Your expertise in large language models (LLMs), prompt systems, evaluation frameworks, and algorithm pipelines will enable you to create innovative prototypes that can be productized into robust, reliable software.

This role is ideal for individuals with a strong background in computer science, machine learning, or related fields, who have a deep understanding of LLMs, fine-tuning, reinforcement learning, and software engineering principles.

Your responsibilities will include:

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

You will also be responsible for developing and benchmarking improvements in LLM architecture, fine-tuning workflows, and generation control, optimizing for performance, resource efficiency, and interpretability.

Additionally, you will own infrastructure, including data pipelines, experiment sandbox, model serving, batch/online inference, logging, and CI/CD, treating experiments as software with automated tests, clear versioning, reproducibility, and documentation.

Lastly, you will create frameworks to automate scientific cycles: data generation, metric computation, model comparison dashboards, and enable internal teams to run reproducible experiments at scale.

Required Skills and Qualifications

Key qualifications for this role include:

  • Master's or Ph.D. in CS, ML, or related field
  • 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
Benefits

To measure success, we look for:

  • Prototypes demonstrating clear scientific advances
  • Papers submitted or draft-ready internal research docs
  • Experimental pipelines and prototypes are production-ready within sprint cycles
  • R&D code drives real product features with high user adoption, improved reliability, or performance

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