AI ML Engineer ( RAG )

3 - 8 years

6 - 12 Lacs

Posted:7 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Hello There!

Casepoint Pvt. Ltd., Surat..!

About Company:

Casepoint provides full eDiscovery capabilities through a powerful, secure, cloud-based platform. We are repeatedly chosen by leading law firms and multinational corporations for their largest matters. On an upward trajectory for almost a decade, Casepoint is looking to expand its team globally. Team cooperation, work hard, play hard attitude, open communication, and kindness mark Casepoint’s culture.

Senior

Key Responsibilities:

  • Design, develop, and deploy

    machine learning and AI models

    , with a strong focus on

    Generative AI

    applications.
  • Implement

    model fine-tuning

    ,

    Retrieval Augmented Generation (RAG)

    , and

    prompt engineering

    techniques.
  • Integrate and optimize

    open-source and commercial AI models

    (e.g.,

    Hugging Face

    ,

    OpenAI

    ,

    Claude

    ,

    Gemini

    ,

    LlamaIndex

    ,

    LangChain

    ).
  • Build and manage

    data pipelines

    for ingestion, transformation, and model training.
  • Enhance

    data collection and preprocessing

    methods to improve model accuracy and robustness.
  • Work with

    vector databases

    and embeddings to enable semantic search and intelligent retrieval.
  • Collaborate with cross-functional teams to integrate ML models into production systems and monitor performance.
  • Develop and maintain

    automated model evaluation, retraining, and anomaly detection systems

    .
  • Conduct experiments, evaluate model results, and present insights clearly to stakeholders.

Required Skills:

  • Strong programming skills in

    Python

    and libraries such as

    NumPy

    ,

    Pandas

    ,

    Scikit-learn

    , and

    PyTorch/TensorFlow

    .
  • Hands-on experience with

    Generative AI frameworks

    ,

    model fine-tuning

    , and

    RAG pipelines

    .
  • Experience integrating

    LLMs

    via APIs or frameworks (

    LangChain

    ,

    LlamaIndex

    , etc.).
  • Familiarity with

    vector storage solutions

    (e.g., FAISS, Pinecone, Chroma).
  • Sound understanding of

    data preprocessing

    ,

    feature engineering

    , and

    model evaluation

    techniques.
  • Exposure to

    cloud environments

    (AWS, GCP, or Azure) and

    MLOps

    tools is a plus.

Copensation & Culture:

great culture builds great products

Experience:

Location:

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Casepoint

Legal Technology

Arlington

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