A
Generative AI Engineer
Ampcus Inc
1 hour ago
Full-time
On-site
Chantilly, Virginia, United States
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.
#J-18808-Ljbffr
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.
#J-18808-Ljbffr