Job Title: LLM Engineer

Experience: 5+ Years (Relevant: 46 Years)

Location: Gurgaon

Looking for Immediate Joiners only.

About the Role:

We are seeking a highly skilled and experienced LLM Engineer to join our cutting-edge GenAI team. The ideal candidate will have a strong background in Python, Prompt Engineering, Agentic AI, and frameworks like LangGraph. You will play a key role in developing, deploying, and optimizing Large Language Model (LLM)-based applications, agents, and pipelines to solve real-world problems and drive innovation in intelligent systems.

Key Responsibilities:

- Design, build, and deploy scalable applications using Large Language Models (LLMs).

- Develop intelligent agents using LangGraph and other agentic AI techniques.

- Create and optimize prompts to improve LLM task performance across multiple domains.

- Collaborate with cross-functional teams including data scientists, ML engineers, and product managers to integrate GenAI solutions into products.

- Fine-tune and evaluate LLMs for domain-specific tasks and use cases.

- Contribute to building internal tools, libraries, and reusable components for GenAI development.

- Monitor, evaluate, and continuously improve system performance, accuracy, and safety.

Required Skills & Experience:

- 46 years of hands-on experience in building and deploying GenAI/LLM-based solutions.

- Strong proficiency in Python and associated libraries for NLP and LLMs (e.g., LangChain, Transformers, LangGraph).

- Deep understanding of Prompt Engineering and its application in production systems.

- Experience building Agentic AI systems and workflow-driven intelligent applications.

- Solid understanding of LLMs (e.g., GPT, LLaMA, Claude) and their capabilities/limitations.

- Familiarity with model evaluation metrics and ethical considerations around LLM use.

- Strong problem-solving skills, communication, and the ability to work in an agile environment.

Preferred Qualifications:

- Experience working with vector databases (e.g., FAISS, Pinecone).

- Exposure to knowledge retrieval (RAG), few-shot learning, or fine-tuning.

- Contributions to open-source GenAI projects or frameworks.

- Experience deploying LLMs at scale on cloud platforms (AWS, GCP, Azure).