Gen AI Engineer - Lead

5 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description

Lead Generative AI Engineer


Roles and Responsibilities:

  • Lead the design, development, and fine-tuning of

    LLMs

    for tasks such as text generation, summarization, classification, Q&A, and dialogue systems.
  • Develop and apply

    Vision-Language Models (VLMs)

    for tasks like image captioning, VQA, multi-modal retrieval, and grounding.
  • Work on

    Computer Vision

    tasks including image generation, detection, segmentation, and manipulation using SOTA deep learning techniques.
  • Leverage frameworks like

    Transformers, Diffusion Models, and CLIP

    to build and fine-tune multi-modal models.
  • Fine-tune open-source LLMs and VLMs (e.g., LLaMA, Mistral, Gemma, Qwen, MiniGPT, Kosmos, etc.) using task-specific or domain-specific datasets.
  • Design

    data pipelines

    , model training loops, and evaluation metrics for generative and multi-modal AI tasks.
  • Optimize model performance for inference using techniques like quantization, LoRA, and efficient transformer variants.
  • Collaborate cross-functionally with product, backend, and ML ops teams to ship models into production.
  • Stay current with the latest research and incorporate emerging techniques into product pipelines.


Requirements:

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
  • 3–5 years

    of hands-on experience in building, training, and deploying deep learning models, especially in

    LLM, VLM

    , and/or

    CV

    domains.
  • Strong proficiency with

    Python

    ,

    PyTorch

    (or TensorFlow), and libraries like

    Hugging Face Transformers, OpenCV, Datasets, LangChain, etc.

  • Deep understanding of

    transformer architecture

    ,

    self-attention mechanisms

    ,

    tokenization

    ,

    embedding

    , and

    diffusion models

    .
  • Experience with

    LoRA

    ,

    PEFT

    ,

    RLHF

    ,

    prompt tuning

    , and

    transfer learning

    techniques.
  • Experience with

    multi-modal datasets

    and

    fine-tuning vision-language models

    (e.g., BLIP, Flamingo, MiniGPT, Kosmos, etc.).
  • Familiarity with

    MLOps tools

    , containerization (Docker), and model deployment workflows (e.g., Triton Inference Server, TorchServe).
  • Strong problem-solving, architectural thinking, and team mentorship skills.


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