AI/ ML OPs Engineer

Job Title:

Location:

Duration:

About the Role

AI/ML Ops Engineer

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 pipeline

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.