Generative AI Engineer

TAC Security; Delhi; On-site; INR 1,800,000 - 2,500,000; Full time

Job summary

A leading technology company in Delhi is seeking a highly skilled Generative AI / LLM Engineer with deep hands-on experience in building advanced language-model systems. The ideal candidate will design and optimize LLM fine-tuning pipelines, develop retrieval-augmented generation systems, and ensure robust AI safety. Strong expertise in LLM fine-tuning techniques and a solid understanding of AI frameworks is required for this mid-senior level full-time position.

Qualifications

• Strong hands-on experience with LLM fine-tuning techniques like LoRA and RLHF.
• Deep expertise with RAG frameworks and retrieval pipelines.
• Practical experience with Model Context Protocol for tool integration.
• Solid understanding of transformer architectures and multimodal systems.
• Proficiency with vector databases such as Pinecone or Weaviate.

Responsibilities

• Design and optimize LLM fine-tuning pipelines.
• Build and maintain retrieval-augmented generation systems.
• Develop applications using Model Context Protocol.
• Architect workflows using various agent frameworks.
• Ensure AI safety through rigorous testing and evaluation.

Skills

LLM fine-tuning

RAG frameworks

Model Context Protocol (MCP)

agent frameworks

transformer architectures

vector databases

AI safety

Tools

Python

MLOps tooling

Job description

Overview

We are seeking a highly skilled Generative AI / LLM Engineer with deep hands‑on experience in building, fine‑tuning, evaluating, and deploying advanced language‑model and agentic systems. The ideal candidate has strong technical expertise across LLM training paradigms, retrieval‑augmented pipelines, agent frameworks, and AI safety evaluation.

Key Responsibilities

• Design, implement, and optimize LLM fine‑tuning pipelines including LoRA, QLoRA, Supervised Fine‑Tuning (SFT), and RLHF.
• Build and maintain RAG (Retrieval‑Augmented Generation) systems using frameworks such as LangChain, LlamaIndex, and custom retrieval layers.
• Develop, integrate, and extend applications using Model Context Protocol (MCP).
• Architect and deploy agentic workflows using frameworks like OpenAI Swarm, CrewAI, AutoGen, or custom agent systems.
• Work with generative AI architectures, including transformer‑based and multimodal models.
• Implement scalable storage, embedding, and similarity search using vector databases (Pinecone, Weaviate, Milvus, Chroma).
• Ensure robust AI safety, including red‑team­ing, adversarial testing, and evaluation of model behaviour.
• Collaborate with cross‑functional teams to deliver end‑to‑end AI‑driven features and products.
• Monitor performance, reliability, and quality of deployed AI systems, optimising continuously.

Required Skills & Experience

• Strong, hands‑on experience with LLM fine‑tuning: LoRA, QLoRA, SFT, RLHF.
• Deep expertise with RAG frameworks and retrieval pipelines (LangChain, LlamaIndex, custom retrieval layers).
• Practical experience with MCP (Model Context Protocol) for tool integration and orchestration.
• Proven work with agent frameworks (OpenAI Swarm, CrewAI, AutoGen, or custom agent systems).
• Solid understanding of transformer architectures, generative AI models, and multimodal systems.
• Proficiency with vector DBs: Pinecone, Weaviate, Milvus, Chroma.
• Strong grounding in AI safety, red‑team­ing strategies, evaluation methodologies, and risk assessment.
• Experience with Python, distributed systems, and MLOps tooling is a plus.

Nice to Have

• Experience with GPU optimisation, quantification, or model distillation.
• Contributions to open‑source LLM or agent‑framework ecosystems.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerisation.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function & Industries

• Information Technology
• Data Security Software Products