Gen AI Research Engineer

Experience: 38 years
Location: Pune
About The Role
We are looking for a highly skilled Generative AI Engineer with strong expertise in Large Language
Models (LLMs), model fine-tuning, Reinforcement Learning (RL/RLHF), and RAG-based systems. The
ideal candidate must have a deep understanding of how LLMs work internally and proven hands-on
experience building, customizing, optimizing, and deploying production-grade generative AI systems.
This role requires both strong research depth and production engineering experience.
Key Responsibilities
?Design, build, and deploy LLM-powered applications in production.
?Fine-tune open-source foundation models (LLaMA, Mistral, Falcon, etc.) for domain-specific
use cases.
?Implement parameter-efficient fine-tuning (LoRA, QLoRA, PEFT).
?Develop and implement Reinforcement Learning pipelines, including RLHF and PPO.
?Build and optimize RAG (Retrieval-Augmented Generation) systems.
?Design and implement AI agents and multi-step reasoning workflows.
?Develop scalable training and inference pipelines using GPUs.
?Benchmark, evaluate, and improve LLM performance.
?Implement evaluation frameworks and model monitoring systems.
?Collaborate cross-functionally to integrate AI into enterprise systems.
Required Skills & Qualifications (Must Have)
LLM & Generative AI Expertise
?Deep understanding of Transformer architecture, attention mechanisms, embeddings, and
tokenization.
?Clear Understanding Of LLM Training Lifecycle

• Pre-training
  

oSupervised Fine-Tuning (SFT)

• Alignment
• RLHF
  

Strong Hands-on Experience With

• PyTorch
• Hugging Face Transformers
• Fine-tuning large language models
• LoRA / QLoRA / PEFT
  

?Experience implementing Reinforcement Learning techniques

• PPO
• Policy gradients

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• Reward modeling
  

?Experience building RAG pipelines end-to-end.
?Experience building AI agents / tool-using LLM systems.
?Understanding of evaluation techniques for generative models.
?Experience working with multi-modal models (text + vision is a plus but preferred).
?Strong Python programming skills.
Infrastructure & Deployment

• Hands-on experience with GPU-based training and inference.
• Experience with distributed training and mixed precision training.
• Familiarity with Docker and Kubernetes.
• Experience deploying models as APIs/services.
• Experience with vector databases (FAISS, Pinecone, Weaviate).
• Experience with cloud platforms (AWS / GCP / Azure).
  

What Were Looking For
?Strong research-oriented mindset.
?Ability to read and implement research papers.
?Strong debugging and optimization skills.
?Clear understanding of cost, latency, and scaling trade-offs.
?Strong communication and ownership.