Senior AI Engineer-Mumbai(Lower Parel)

3 days ago

Mumbai, Maharashtra, India Quantal AI Full time

Location: Mumbai – Lower Parel

Experience: 3+ Years

Notice Period: Immediate or 30 days

Key Responsibilities:

  • Lead the end-to-end development of Generative AI solutions with a strong focus on LLMs, RAG, and Deep Learning.
  • Manage and mentor a team of AI/ML engineers, provide technical leadership, and ensure best engineering practices.
  • Collaborate directly with international clients, gather requirements, present solutions, and ensure alignment with business goals.
  • Architect scalable Retrieval-Augmented Generation (RAG) systems using frameworks like
    Langchain, LlamaIndex, Langraph
    , and implement advanced embedding techniques.
  • Drive pre-training, fine-tuning, and deployment of LLMs (e.g., GPT, LLaMA, Falcon) in real-world use cases.
  • Build robust backend APIs using FastAPI, Flask, and integrate with GenAI services at scale.
  • Lead the design of vector search solutions, prompt engineering, and context store development.
  • Stay updated with the latest in GenAI research and incorporate best practices into production-level solutions.

Mandatory Skills & Expertise:

  • 3+ years of experience in AI/ML, with Generative AI and LLM applications.
  • Proven experience managing small to mid-sized technical teams, conducting performance reviews, and enabling growth.
  • Prior experience working with international clients, managing expectations, and communicating complex AI concepts to non-technical stakeholders.
  • Expertise in RAG frameworks (
    Langchain, LlamaIndex, Langraph
    ) and vector databases (Pinecone, Faiss, Waviate).
  • Strong command of Python and AI/ML libraries like PyTorch, TensorFlow, Transformers, Streamlit.
  • Hands-on experience in prompt engineering, in-context learning, and embedding search pipelines.
  • Deep understanding of transformer models, model optimization, feature engineering, and data curation.
  • Experience in building scalable GenAI APIs and services using REST, FastAPI, and containerized environments.

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