AI System Developer

2 days ago

India beBeeInnovation Full time ₹ 15,00,000 - ₹ 20,00,000
Unlock AI InnovationThis is a key role that merges groundbreaking AI research with hands-on system development. As an expert in this field, you will conduct research, build, validate, and ship AI capabilities end-to-end. The ideal candidate is a bridge between science and software.Key Responsibilities:
  • Research Prototyping: Design experiments in LLM training, LLM agent architectures, fine tuning, RL, and new evaluation methodologies.
  • Build and Refine Prototypes: Create code-first prototypes to test hypotheses and iterate rapidly.
Model & Architecture Innovation
  • Develop Improvements: Optimize for performance, resource efficiency, and interpretability in LLM architecture and fine-tuning workflows.
  • Optimize Generation Control: Develop improvements in generation control.
Production Engineering
  • Own Infrastructure: From data pipelines and experiment sandbox to model serving, batch/online inference, logging, and CI/CD.
  • Treat Experiments as Software: Automated tests, clear versioning, reproducibility, and documentation.
Scientific Experiment Tooling
  • Automate Scientific Cycles: Data generation, metric computation, model comparison dashboards.
  • Enable Teams: Run reproducible experiments at scale.
Knowledge Sharing & Publication
  • Write Up Findings: Internal memos, tech blogs, or scholarship-worthy results.
  • Critique Sessions: Participate in code and science critique sessions with peers.
Qualifications

Mastery of CS, ML, or related (or equivalent experience)

  • LLM Expertise: Deep familiarity with LLMs, fine-tuning, reinforcement learning.
  • Python Skills: Expert in Python + frameworks like PyTorch or JAX; experience with Hugging Face or deployed LLM APIs.
  • Software Engineering Intuition: Modular design, CI/CD, containerization, automated testing.
  • Track Record: Demonstrated track record in both research and production settings.
How You'll Measure Success

Research Innovation: Clear scientific advances (e.g., better evaluation metrics or prompt robustness).

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

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

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