Job Description:
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.