AI ML Engineer ( RAG )

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: