Generative AI Engineer

Experience 3-8 Years Role Overview We 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. The 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. Key Responsibilities MCP (Model Context Protocol) Design and develop MCP servers and tools for LLM-driven applications.Implement tool-calling frameworks and agent integrations.Enable secure exposure of enterprise capabilities to AI assistants.Manage authentication, authorization, and governance of MCP services.Optimize context-sharing mechanisms between AI models and enterprise systems.RAG Engineering Design and implement enterprise-grade RAG architectures.Build document ingestion, chunking, embedding, indexing, and retrieval pipelines.Integrate vector databases and semantic search solutions.Improve answer quality through reranking, hybrid search, and prompt optimization.Monitor retrieval accuracy, latency, and hallucination rates.Evaluate and implement advanced retrieval techniques.Connectors & Integrations Develop connectors for enterprise systems such as:SharePointMicrosoft GraphAzure StorageSalesforceServiceNowSAPDatabases (SQL/NoSQL)Internal APIsBuild API integration frameworks and data synchronization pipelines.Implement event-driven and real-time data access patterns.Ensure scalability, security, and data compliance requirements.AI Platform Development Collaborate with Data Scientists, AI Engineers, and Product Teams.Build reusable AI integration frameworks and SDKs.Develop observability, monitoring, and governance solutions.Implement CI/CD pipelines for AI services.Support production deployment and operational excellence. Required Skills AI & LLM Technologies Strong understanding of Large Language Models (GPT, Claude, Gemini, Llama, etc.)Hands-on experience with:LangChainLlamaIndexSemantic KernelAzure AI Foundry / Azure OpenAIAI Agents and Tool CallingRAG Expertise Embeddings and vector searchSemantic search and hybrid retrievalMetadata filteringDocument processing pipelinesEvaluation frameworks for RAG systemsMCP Knowledge Understanding of MCP architecture and ecosystemMCP server development and tool registrationContext management and agent integrationIntegration Development REST APIsGraphQL APIsOAuth 2.0 / OpenID ConnectMicrosoft Graph APIEnterprise system integration patternsProgramming Skills Python (mandatory)JavaScript / TypeScriptFastAPI, Flask, Node.jsSDK and API developmentData & Search Technologies Azure AI SearchPineconeWeaviateChromaElasticsearch / OpenSearchSQL and NoSQL databasesCloud Platforms Microsoft Azure (preferred)AWS or Google Cloud (good to have)Docker and KubernetesCI/CD pipelines Preferred Qualifications Experience building Microsoft Copilot extensions and plugins.Experience with Copilot Studio and Microsoft Graph Connectors.Understanding of enterprise security and governance frameworks.Exposure to Agentic AI and multi-agent architectures.Knowledge of MLOps and AI observability tools.Azure AI Engineer, Azure Developer, or related certifications. Success Metrics Improved retrieval accuracy and response quality.Reduced AI hallucinations through optimized RAG pipelines.Successful integration of enterprise data sources.High availability and performance of MCP services.Adoption of AI solutions across business functions. Nice-to-Have Experience Microsoft 365 Copilot extensibilityGraph ConnectorsAzure AI SearchCopilot StudioOpenTelemetryPrompt EngineeringMulti-Agent SystemsKnowledge GraphsEvent-Driven Architectures