Generative AI Engineer

Job Description

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

• 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.
• 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:
• SharePoint
• Microsoft Graph
• ServiceNow
• SAP
• Databases (SQL/NoSQL)
• Internal APIs
• Build 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:
• LangChain
• Semantic Kernel
• AI Agents and Tool Calling

RAG Expertise

• Embeddings and vector search
• Semantic search and hybrid retrieval
• Evaluation frameworks for RAG systems

MCP Knowledge

• Understanding of MCP architecture and ecosystem
• MCP server development and tool registration
• Context management and agent integration

Integration Development

• REST APIs
• GraphQL APIs
• OAuth 2.0 / OpenID Connect
• Microsoft Graph API

Programming Skills

• Python (mandatory)
• FastAPI, Flask, Node.js
• SDK and API development

Data & Search Technologies

• Pinecone
• Weaviate
• Chroma
• Elasticsearch / OpenSearch
• SQL and NoSQL databases
• AWS or Google Cloud (good to have)
• Docker and Kubernetes

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.

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.