Lead Python Developer + Artificial intelligence

Role Overview: You will be responsible for designing, developing, and maintaining applications in Python. Your main focus will be on implementing RAG pipelines by integrating LLMs with enterprise and external data sources, developing MCP-based integrations, building APIs and microservices for AI-powered search, summarization, and conversational AI, creating document ingestion pipelines, and managing embeddings with vector databases. Your collaboration with AI engineers, architects, and data teams will ensure scalable deployment of RAG/MCP solutions. Additionally, you will optimize application performance, security, and scalability for production-grade AI systems while staying updated with AI frameworks, MCP standards, and cloud AI services. Key Responsibilities: - Design, develop, and maintain applications in Python. - Implement RAG pipelines by integrating LLMs 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. - 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. Qualifications Required: - 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 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. Additional Company Details: There are no additional company details provided in the job description. Role Overview: You will be responsible for designing, developing, and maintaining applications in Python. Your main focus will be on implementing RAG pipelines by integrating LLMs with enterprise and external data sources, developing MCP-based integrations, building APIs and microservices for AI-powered search, summarization, and conversational AI, creating document ingestion pipelines, and managing embeddings with vector databases. Your collaboration with AI engineers, architects, and data teams will ensure scalable deployment of RAG/MCP solutions. Additionally, you will optimize application performance, security, and scalability for production-grade AI systems while staying updated with AI frameworks, MCP standards, and cloud AI services. Key Responsibilities: - Design, develop, and maintain applications in Python. - Implement RAG pipelines by integrating LLMs 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. - 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. Qualifications Required: - 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 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. Additional Company Details: There are no additional company details provided in the job description.