This role is for one of the Weekday's clients

Salary range: Rs 4000000 - Rs 7000000 (ie INR 40-70 LPA)

Experience: 5+ yrs

Location: Noida, Uttar Pradesh, India

Job Type: Full-Time

We are looking for an experienced Machine Learning Engineer with strong expertise in Large Language Models (LLMs) to build, fine-tune, and optimize AI models for domain-specific applications. This role is ideal for professionals who enjoy working on cutting-edge generative AI technologies, adapting foundation models for real-world use cases, and delivering scalable AI solutions in a fast-paced, innovation-driven environment.

As an ML Engineer, you will own the complete model adaptation lifecycle—from dataset preparation and fine-tuning to evaluation, optimization, and deployment. You will work with modern open-source LLMs, implement efficient fine-tuning techniques, and develop AI models that deliver high-quality, context-aware outputs. This is a high-ownership role where you will collaborate with cross-functional engineering and product teams to build production-ready AI systems while continuously improving model quality, efficiency, and scalability.

Requirements

Key Responsibilities

• Fine-tune and optimize Large Language Models (LLMs) for domain-specific tasks such as question answering, content generation, summarization, and intelligent automation.
• Own end-to-end model adaptation workflows, including dataset preparation, training, hyperparameter tuning, evaluation, and model versioning.
• Implement efficient fine-tuning approaches such as LoRA, QLoRA, DoRA, adapters, and other parameter-efficient training techniques.
• Build and optimize reinforcement learning and preference optimization pipelines using techniques such as RLHF, DPO, PPO, and reward modeling.
• Develop scalable training pipelines using distributed and multi-GPU environments.
• Optimize GPU utilization through DeepSpeed, FSDP, mixed precision, gradient checkpointing, and other performance optimization techniques.
• Design and maintain multilingual and instruction-tuning datasets to improve model performance across diverse use cases.
• Evaluate model quality using automated benchmarks, task-specific metrics, human evaluations, and regression testing.
• Continuously assess emerging open-source foundation models and recommend suitable architectures for production adoption.
• Define model performance benchmarks, monitor quality metrics, and drive continuous optimization across multiple model versions.
• Collaborate with engineering, product, and data teams to integrate AI models into scalable production environments.
• Contribute to AI infrastructure, model deployment, and best practices for enterprise-grade machine learning systems.

What Makes You a Great Fit

• 5+ years of experience in Machine Learning, Deep Learning, or AI Engineering.
• Strong hands-on expertise in Python, PyTorch, and the Hugging Face ecosystem, including Transformers, PEFT, and TRL.
• Proven experience fine-tuning and optimizing Large Language Models using techniques such as LoRA, QLoRA, SFT, DPO, RLHF, or similar methods.
• Experience working with distributed training, multi-GPU environments, and large-scale model optimization.
• Strong understanding of model evaluation methodologies, benchmarking, and performance optimization.
• Hands-on experience with DeepSpeed, Megatron-LM, FSDP, or comparable large-scale training frameworks.
• Knowledge of AI/ML pipelines, dataset preparation, model deployment, and production AI systems.
• Familiarity with Hugging Face tools, GPU optimization, and open-source LLM ecosystems is highly desirable.
• Strong analytical, debugging, and problem-solving skills with a passion for building high-quality AI solutions.
• Comfortable working in high-ownership, fast-moving environments with the ability to adapt quickly, solve ambiguous problems, and contribute to building innovative AI products from the ground up.