Description

Key Skills: Machine Learning, AI Artificial intelligence, LLM

Roles and Responsibilities:

• Design and implement LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, and experiment tracking
• Build evaluation systems for LLMs and multimodal models using offline benchmarks, task-specific harnesses, and comparison workflows
• Integrate human-in-the-loop and AI-augmented evaluation signals into model development loops
• Diagnose model behavior, training instability, metric inconsistencies, and evaluation drift across experiments
• Partner with research scientists, data engineers, and technical stakeholders to translate evaluation findings into measurable model improvements

Skills Required:

• 2 to 6 years of relevant industry or research engineering experience in ML/AI systems
• Strong hands-on experience with machine learning engineering and LLM post-training workflows, including supervised fine-tuning or preference optimization methods
• Production-grade Python development with practical experience in PyTorch, JAX, TensorFlow, or Hugging Face-based model workflows
• Experience building scalable evaluation and experimentation systems with reproducibility, metrics logging, and distributed training considerations
• Ability to work with large-scale data processing pipelines and ML infrastructure for model training and evaluation

Good to Have:

• Experience with multimodal models, long-context evaluation, agentic or multi-turn harnesses, LLM safety, alignment, robustness, or open-source ML tooling

Education: B.E., B.Tech, B.Tech M.Tech (Dual), or equivalent degree in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative field

Key Skills

Education

Any Graduate