AI/ML Engineer

2 hours ago

Ahmedabad, Gujarat, India The TFPL Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Job description:

Job Title: AI/ML Ops Engineer

Location: Ahmedabad - Onsite

Duration: 2-5 years experience

About the Role

We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications.

Key Responsibilities

  • Develop and maintain machine learning models to forecast user behavior using structured time-series data.
  • Build and optimize end-to-end regression pipelines using advanced libraries such as 
    CatBoost
    , 
    XGBoost
    , and 
    LightGBM
    .
  • Design and implement 
    RAG (Retrieval-Augmented Generation)
     pipelines for enterprise chatbot systems utilizing tools like 
    LangChain
    , 
    LLM Router
    , or custom-built orchestrators.
  • Work with 
    vector databases
     for semantic document retrieval and reranking.
  • Integrate external APIs into 
    LLM workflows
     to enable tool/function calling capabilities.
  • Package and deploy ML services using tools such as 
    Docker
    , 
    FastAPI
    , or 
    Flask
    .
  • Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices.

Core Technologies & Tools:

  • Languages:
     Python (primary), Bash, SQL
  • ML Libraries:
     scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow
  • LLM & RAG Tools:
     LangChain, Hugging Face Transformers, LlamaIndex, LLM Router
  • Vector Stores:
     FAISS, Weaviate, Chroma, Pinecone
  • Deployment & APIs:
     Docker, FastAPI, Flask, Postman
  • Infrastructure & Version Control:
     Git, GitHub, CI/CD pipelines

Preferred Qualifications

  • Proven experience in ML Ops, AI infrastructure, or productionizing ML models.
  • Strong understanding of large-scale ML system design and deployment strategies.
  • Experience working with vector databases and LLM-based applications in production.

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