Artificial Intelligence and Machine Learning

Responsibilities

• Design, train, and deploy machine learning and deep learning models for NLP, vision, or recommendation systems.
• Develop robust APIs using Node.js (Express/Nest.js ) and Python FastAPI for serving AI/ML models.
• Fine-tune and serve Hugging Face Transformers and LLMs(BERT, GPT, Llama, etc. ).
• Build data ingestion and preprocessing pipelines using Python, Pandas, and FastAPI.
• Integrate LLMs and AI agents using frameworks such as LangChain, LlamaIndex, or OpenAI API.
• Implement MLOps workflows including model tracking, CI/CD, and monitoring with tools like MLflow or DVC.
• Deploy and scale models using Docker, Kubernetes, AWS/GCP/Azure, or serverless architectures.
• Collaborate with cross-functional teams (data, frontend, product) to build and ship AI-driven features.
  

Requirements

• Backend: Node.js (Express/Nest.js ), FastAPI.
• ML/AI: Python, TensorFlow, PyTorch, scikit-learn.
• AI/ML Tools: Hugging Face Transformers, LangChain, OpenAI API, LlamaIndex.
• Data Handling: Pandas, NumPy, SQL/NoSQL databases.
• DevOps/MLOps: Docker, Kubernetes, Git, MLflow, DVC.
• API Development: REST, GraphQL, WebSocket.
• Cloud Deployment: AWS (SageMaker/Lambda), GCP (Vertex AI), Azure ML.
• Strong understanding of LLM architecture, embeddings, and vector databases such as Pinecone, FAISS, or Milvus.
• Experience with TypeScript for Node.js backend.
• Worked on chatbots, RAG (Retrieval-Augmented Generation)systems, or AI assistants.
• Familiarity with FastAPI async endpoints for real-time inference.
• Exposure to model quantisation optimisation for faster inference.
• Experience integrating FastAPI microservices into Node.js ecosystems.
• B. Tech / M. Tech / MCA in Computer Science, Artificial Intelligence, or a related field.