Lead Python Developer + Artificial intelligence_ Ex: 8+ Years

6 days ago

Pune, Maharashtra, India Atyeti Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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.

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