Generative AI Engineer

2 days ago

Bengaluru, India TRUGlobal Full time

Job Description: Generative AI Engineer

Position Overview

We are seeking a highly skilled
Generative AI Engineer
with expertise in
Agentic AI
,
LLMs (Large Language Models)
, and
Retrieval-Augmented Generation (RAG)
to design, build, and deploy AI-powered solutions. The ideal candidate will have 6+ years of experience in AI/ML with strong hands-on expertise in building scalable GenAI applications, integrating with enterprise systems, and optimizing model performance for real-world use cases.

Key Responsibilities

  • Design and implement
    Generative AI solutions
    leveraging LLMs and Agentic AI frameworks.
  • Develop and optimize
    RAG pipelines
    to enhance accuracy and context relevance of AI applications.
  • Build, fine-tune, and deploy
    LLM-based applications
    for tasks such as text generation, summarization, Q&A, and conversational AI.
  • Architect
    agentic workflows
    enabling autonomous AI agents to plan, reason, and execute tasks.
  • Collaborate with product, data, and engineering teams to integrate AI models into scalable systems.
  • Evaluate, benchmark, and optimize LLMs for performance, latency, and cost.
  • Stay current with the latest advancements in Generative AI, multi-agent systems, and foundation models.
  • Ensure adherence to responsible AI principles, security, and compliance guidelines.

Required Skills & Qualifications

  • 6+ years of experience
    in AI/ML engineering, with at least 2+ years in Generative AI/LLM development.
  • Strong proficiency in
    LLM frameworks & APIs
    (OpenAI, Anthropic, Hugging Face, Llama, Mistral, etc.).
  • Expertise in
    RAG (Retrieval-Augmented Generation)
    and vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus).
  • Hands-on experience with
    Agentic AI frameworks
    (LangChain, LlamaIndex, AutoGen, Haystack Agents, or similar).
  • Proficiency in
    Python
    and ML libraries (PyTorch, TensorFlow, Transformers).
  • Experience with
    cloud platforms
    (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
  • Strong background in
    MLOps practices
    – model deployment, monitoring, and optimization.
  • Knowledge of
    prompt engineering, fine-tuning, and instruction tuning
    techniques.
  • Excellent problem-solving and communication skills with ability to work in cross-functional teams.

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