Generative AI

2 days ago

Mohali, Punjab, 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 optimise
    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, optimizing 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, quantisation, or model distillation.
  • Contributions to open-source LLM or agent-framework ecosystems.
  • Familiarity with cloud platforms (AWS, Azure, GCP) and containerisation.

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