Generative AI Engineer

Job DescriptionExperience\n3-8 Years\n\nRole Overview\nWe are seeking a highly skilled MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), and Connectors Engineer to design, build, and optimize AI-powered solutions that integrate enterprise data sources with Large Language Models (LLMs).

The ideal candidate will have hands-on experience with AI platforms, enterprise integrations, vector databases, retrieval pipelines, APIs, and modern AI application architectures.\nThe role will focus on enabling secure, scalable, and context-aware AI experiences by developing MCP servers, building RAG pipelines, and integrating enterprise systems through custom connectors.\n\nKey Responsibilities\nMCP (Model Context Protocol)\n\n Design and develop MCP servers and tools for LLM-driven applications.\n Implement tool-calling frameworks and agent integrations.\n Enable secure exposure of enterprise capabilities to AI assistants.\n Manage authentication, authorization, and governance of MCP services.\n Optimize context-sharing mechanisms between AI models and enterprise systems.\n\nRAG Engineering\n\n Design and implement enterprise-grade RAG architectures.\n Build document ingestion, chunking, embedding, indexing, and retrieval pipelines.\n Integrate vector databases and semantic search solutions.\n Improve answer quality through reranking, hybrid search, and prompt optimization.\n Monitor retrieval accuracy, latency, and hallucination rates.\n Evaluate and implement advanced retrieval techniques.\n\nConnectors & Integrations\n\n Develop connectors for enterprise systems such as:\n SharePoint\n Microsoft Graph\n Azure Storage\n Salesforce\n ServiceNow\n SAP\n Databases (SQL/NoSQL)\n Internal APIs\n Build API integration frameworks and data synchronization pipelines.\n Implement event-driven and real-time data access patterns.\n Ensure scalability, security, and data compliance requirements.\n\nAI Platform Development\n\n Collaborate with Data Scientists, AI Engineers, and Product Teams.\n Build reusable AI integration frameworks and SDKs.\n Develop observability, monitoring, and governance solutions.\n Implement CI/CD pipelines for AI services.\n Support production deployment and operational excellence.\n\n\nRequired Skills\nAI & LLM Technologies\n\n Strong understanding of Large Language Models (GPT, Claude, Gemini, Llama, etc.)\n Hands-on experience with:\n LangChain\n LlamaIndex\n Semantic Kernel\n Azure AI Foundry / Azure OpenAI\n AI Agents and Tool Calling\n\nRAG Expertise\n\n Embeddings and vector search\n Semantic search and hybrid retrieval\n Metadata filtering\n Document processing pipelines\n Evaluation frameworks for RAG systems\n\nMCP Knowledge\n\n Understanding of MCP architecture and ecosystem\n MCP server development and tool registration\n Context management and agent integration\n\nIntegration Development\n\n REST APIs\n GraphQL APIs\n OAuth 2.0 / OpenID Connect\n Microsoft Graph API\n Enterprise system integration patterns\n\nProgramming Skills\n\n Python (mandatory)\n JavaScript / TypeScript\n FastAPI, Flask, Node.js\n SDK and API development\n\nData & Search Technologies\n\n Azure AI Search\n Pinecone\n Weaviate\n Chroma\n Elasticsearch / OpenSearch\n SQL and NoSQL databases\n\nCloud Platforms\n\n Microsoft Azure (preferred)\n AWS or Google Cloud (good to have)\n Docker and Kubernetes\n CI/CD pipelines\n\n\nPreferred Qualifications\n\n Experience building Microsoft Copilot extensions and plugins.\n Experience with Copilot Studio and Microsoft Graph Connectors.\n Understanding of enterprise security and governance frameworks.\n Exposure to Agentic AI and multi-agent architectures.\n Knowledge of MLOps and AI observability tools.\n Azure AI Engineer, Azure Developer, or related certifications.\n\n\nSuccess Metrics\n\n Improved retrieval accuracy and response quality.\n Reduced AI hallucinations through optimized RAG pipelines.\n Successful integration of enterprise data sources.\n High availability and performance of MCP services.\n Adoption of AI solutions across business functions.\n\n\nNice-to-Have Experience\n\n Microsoft 365 Copilot extensibility\n Graph Connectors\n Azure AI Search\n Copilot Studio\n OpenTelemetry\n Prompt Engineering\n Multi-Agent Systems\n Knowledge Graphs\n Event-Driven Architectures\n