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Data Scientist- Generative AI Specialist

3 years

0 Lacs

Noida Uttar Pradesh India

Posted:2 days ago| Platform: Linkedin logo

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Skills Required

data ai design cutting retrieval reasoning azure openai orchestration model training customization databricks aws mlflow devops storage python programming deployment

Work Mode

On-site

Job Type

Full Time

Job Description

Job Title:

Location:

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About the Role

Data Scientist – Generative AI Specialist

Key Responsibilities

  • Build, fine-tune, and deploy

    Generative AI

    and

    LLM

    -based solutions.
  • Implement

    Retrieval-Augmented Generation (RAG)

    pipelines, embeddings, and semantic search.
  • Develop and manage

    Knowledge Graphs

    to enhance reasoning and context.
  • Apply

    Prompt Engineering

    best practices to improve AI output quality.
  • Work with

    Azure AI ecosystem

    – Azure OpenAI, Cognitive Services, Azure ML.
  • Leverage

    LangChain

    and other Agentic AI orchestration frameworks for intelligent automation.
  • Utilize

    Hugging Face

    for model training, customization, and deployment.
  • Integrate and optimize AI models using

    FastAPI

    , Databricks, and AWS services.
  • Manage

    MLOps pipelines

    using CI/CD, MLflow, and DevOps tools.
  • Work with vector databases (

    Faiss, Redis

    ) for storage and retrieval of embeddings.
  • Troubleshoot, debug, and improve AI systems for performance and reliability.

Required Skills & Experience

  • 3+ years

    of Data Science experience, with a focus on Generative AI, Agentic AI, LLMs, and NLP.
  • Strong Python programming skills for AI application development.
  • Expertise in

    RAG

    ,

    Knowledge Graphs

    ,

    Prompt Engineering

    .
  • Proficiency with

    Azure OpenAI

    , Cognitive Services, Azure ML.
  • Hands-on experience with

    LangChain

    , Hugging Face, and vector DBs (Faiss, Redis).
  • Familiarity with

    FastAPI

    , Databricks, AWS, MLflow.
  • Solid background in

    MLOps

    , CI/CD pipelines, and AI deployment optimization.

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