Research AI ML Engineer || Noida || Hybrid
- Posted 15 hours ago
- Be among the first 10 applicants
Job Description
- Work in a fast paced environment
- Work with a global firm
Our client is a global technology services organization specializing in AI, data engineering, and advanced machine learning solutions. They partner with leading enterprises and AI innovators to develop, evaluate, and improve next-generation AI models.
Job Description
- 2-3+ years of experience in Machine Learning, AI Systems, or Research Engineering with hands-on exposure to LLMs.
- Strong experience in LLM fine-tuning, post-training workflows, SFT, DPO, RLHF/RLAIF, or foundation model adaptation.
- Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, JAX, and Hugging Face.
- Experience designing and implementing evaluation pipelines, benchmarking frameworks, and experiment tracking systems.
- Strong understanding of ML systems engineering, data pipelines, reproducibility, debugging, and distributed training environments.
We're Looking For Someone Who Has
- Hands-on experience building, fine-tuning, and evaluating LLMs or multimodal AI models.
- Strong software engineering capabilities with production-grade Python development experience.
- Expertise in experimentation, model benchmarking, performance optimization, and evaluation methodologies.
- Ability to collaborate effectively with researchers, data engineers, and technical stakeholders in complex AI environments.
- Strong problem-solving skills with the ability to translate research concepts into scalable engineering solutions.
- A permanent role based in Noida.
- Opportunities to work in the not-for-profit business school sector.
- Exposure to cutting-edge AI/ML technologies.
Quote job ref: JN-092026-7097834
More Info
Key Skills
RLAIF
Research Engineering
data pipelines
SFT
LLMs
DPO
reproducibility
distributed training
Hugging Face
experiment tracking systems
LLM fine-tuning
AI Systems
evaluation pipelines
foundation model adaptation
ML systems engineering
benchmarking frameworks
RLHF
