Lead Python Developer + Artificial intelligence

As an experienced IT professional with over 8 years of experience and at least 1 year in AI, your role will involve the following key responsibilities: - Designing, developing, and maintaining applications in Python. - Implementing RAG pipelines by integrating various LLMs such as OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, etc. with enterprise and external data sources. - Developing MCP-based integrations to connect tools, APIs, and enterprise data systems with LLMs. - Building APIs and microservices for AI-powered search, summarization, and conversational AI. - Creating document ingestion pipelines for PDFs, databases, SharePoint, etc. and managing embeddings with vector databases like Pinecone, Weaviate, FAISS, Qdrant, Azure Cognitive Search, etc. - Collaborating with AI engineers, architects, and data teams to ensure scalable deployment of RAG/MCP solutions. - Optimizing application performance, security, and scalability for production-grade AI systems. - Staying updated with AI frameworks, MCP standards, and cloud AI services. Your required skills and experience for this role include: - 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 like FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search. - Hands-on experience with AI libraries such as LangChain, LlamaIndex, Hugging Face Transformers, or similar. - Strong problem-solving and cross-functional collaboration skills. Good to have: - Experience with containerization tools like 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.,