AI/ML Lead/Architect

AI/ML Engineer (2+ Years Experience)

Job Summary

We are seeking an AI/ML Engineer with 2+ years of hands-on experience in building, deploying, and optimizing AI/ML and Generative AI solutions. The ideal candidate will have expertise in Python, FastAPI, PyTorch, LLM orchestration, vector search, MCP integrations, and scalable AI application development.

Key Responsibilities

• Design, develop, and deploy AI/ML and GenAI applications in production environments.
• Build scalable APIs and AI services using Python, FastAPI, and asynchronous programming.
• Develop and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases such as Pinecone and pgvector.
• Design chunking, embedding, indexing, and hybrid search strategies to improve retrieval accuracy.
• Build and deploy custom MCP servers and clients to integrate LLMs with internal APIs, databases, and enterprise systems.
• Develop agent-based workflows using LangChain, LangGraph, LlamaIndex, and function-calling frameworks.
• Optimize AI solutions for performance, latency, scalability, and cost efficiency.
• Collaborate with cross-functional teams to deliver secure, reliable, and production-ready AI solutions.

Required Skills

• 2+ years of hands-on experience in AI/ML development and deployment.
• Strong programming skills in Python.
• Experience with FastAPI, PyTorch, and asynchronous programming.
• Hands-on experience with MCP architecture, custom MCP servers, and client implementations.
• Experience with vector databases such as Pinecone, pgvector, and semantic/hybrid search.
• Strong knowledge of LLMs, RAG architecture, prompt engineering, and AI agents.
• Experience with LangChain, LangGraph, LlamaIndex, and function calling.
• Knowledge of REST APIs, data pipelines, and database integrations.
• Familiarity with cloud platforms, Docker, CI/CD, and MLOps concepts is preferred.

Preferred Qualifications

• Experience with Azure OpenAI, AWS Bedrock, or similar AI platforms.
• Exposure to fine-tuning open-source LLMs and AI observability tools.
• Understanding of security, governance, and responsible AI practices.