Lead Python Developer + Artificial intelligence_ Ex: 8+ Years

Role & responsibilities

Key Responsibilities:

• Design, develop, and maintain applications in

  Python

  .
• Implement

  RAG pipelines

  by integrating

  LLMs (OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, etc.)

  with enterprise and external data sources.
• Develop

  MCP-based integrations

  to connect tools, APIs, and enterprise data systems with LLMs.
• Build APIs and microservices for

  AI-powered search, summarization, and conversational AI

  .
• Create

  document ingestion pipelines

  (PDFs, databases, SharePoint, etc.) and manage embeddings with

  vector databases (Pinecone, Weaviate, FAISS, Qdrant, Azure Cognitive Search, etc.)

  .
• Collaborate with AI engineers, architects, and data teams to ensure scalable deployment of RAG/MCP solutions.
• Optimize application

  performance, security, and scalability

  for production-grade AI systems.
• Stay updated with

  AI frameworks, MCP standards, and cloud AI services

  .

Required Skills & Experience:

• Minimum of 8 years of IT experience with 1+ years of AI experience
• Strong hands-on experience in

  Python

  .
• Solid understanding of

  OOP, REST APIs, and microservices architecture

  .
• Proven experience with

  LLM-based applications

  and

  RAG (Retrieval-Augmented Generation)

  integration.
• Knowledge and practical implementation of

  Model Context Protocol (MCP)

  for AI tool orchestration.
• Familiarity with

  vector databases

  (FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search).
• Hands-on experience with

  LangChain, LlamaIndex, Hugging Face Transformers

  , or similar AI libraries.
• Strong problem-solving and cross-functional collaboration skills.

Good to Have:

• Experience with

  containerization (Docker, Kubernetes)

  .
• Experience with

  cloud AI services (Azure, AWS, GCP)

  for deployment and scaling.
• Exposure to

  SQL/NoSQL databases

  for structured and unstructured data.
• Prior experience in

  chatbot development, enterprise search, or knowledge management systems

  .
• Understanding of

  MLOps practices

  for AI model deployment and monitoring.