Role : Senior Generative AI Engineer

Location : Gurgaon | Work Mode : Hybrid | Experience : 811 Years

About the Role :

We are looking for a Senior Generative AI Engineer with deep hands-on expertise in building, fine-tuning, and deploying LLM-based and Generative AI applications. The ideal candidate will have strong engineering fundamentals combined with practical experience in designing production-grade GenAI systems.

Key Responsibilities :

- Design, build, and deploy Generative AI applications using LLMs (OpenAI, Anthropic, open-source models like Llama, Mistral, etc.)

- Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, including chunking strategies, embeddings, and retrieval optimization

- Build agentic AI workflows and multi-agent systems using frameworks like LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen

- Fine-tune and customize LLMs/foundation models using techniques like LoRA, QLoRA, PEFT, and RLHF

- Design and implement prompt engineering strategies and evaluation frameworks for LLM outputs

- Work with vector databases (Pinecone, Weaviate, FAISS, Milvus, ChromaDB) for efficient semantic search and retrieval

- Build scalable APIs and microservices to serve GenAI models in production

- Implement MLOps/LLMOps practices model versioning, monitoring, observability, and CI/CD for GenAI pipelines

- Optimize model inference for latency, cost, and throughput (quantization, caching, batching)

- Collaborate with product managers, data scientists, and engineering teams to translate business use cases into GenAI solutions

- Ensure responsible AI practices hallucination mitigation, bias detection, guardrails, and content safety

- Stay current with the fast-evolving GenAI landscape and evaluate new tools, models, and techniques

Required Skills & Qualifications :

- 811 years of overall software/ML engineering experience, with 23+ years of hands-on Generative AI/LLM experience

- Strong proficiency in Python and experience with ML/DL frameworks (PyTorch, TensorFlow, Hugging Face Transformers)

- Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex)

- Practical experience building RAG systems and working with vector databases and embedding models

- Experience with LLM fine-tuning techniques (LoRA, QLoRA, PEFT) and prompt engineering

- Strong understanding of transformer architectures and foundation model internals

- Experience deploying models/APIs on cloud platforms (AWS/Azure/GCP) using services like SageMaker, Bedrock, Azure OpenAI, or Vertex AI

- Familiarity with containerization and orchestration (Docker, Kubernetes)

- Experience with MLOps/LLMOps tools (MLflow, Weights & Biases, LangSmith, etc.)

- Solid understanding of API development (FastAPI/Flask) and microservices architecture

- Strong problem-solving skills and ability to work in a fast-paced, evolving domain

Good to Have :

- Experience with multi-modal AI (vision-language models, speech, etc.)

- Exposure to agentic AI frameworks and autonomous agent design patterns

- Knowledge of model evaluation frameworks (RAGAS, DeepEval, etc.)

- Contributions to open-source GenAI projects or published research/blogs

- Experience with GPU optimization and distributed training/inference

Education : Bachelor's/Master's degree in Computer Science, Engineering, or related field