Generative AI

6 days ago

Delhi India, India TAC Security Full time

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 behavior.
  • 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 containerization.

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