Senior AI Engineer – Agentic AI, LLM & Knowledge Graphs

We are seeking an experienced Senior AI Engineer to design build and operate production-grade AI systems that enable end-to-end customer experiences increase straight-through processing and optimize technology capabilities. The ideal candidate will have hands-on experience delivering LLM and Generative AI solutions beyond the prototype stage with strong software engineering and production operations expertise.

• 610 years of production software engineering experience including hands-on delivery of AI/LLM systems.
  
• Strong Java/JVM engineering experience; Kotlin or Scala preferred.
  
• Strong software engineering practices including Git code reviews automated testing structured logging and clean design principles.
  
• Hands-on experience with GenAI patterns including prompt engineering structured/JSON outputs tool/function calling RAG and agentic workflows.
  
• Experience with Neo4j/Cypher and/or MongoDB Atlas.
  
• Experience with vector search and embeddings for semantic retrieval.
  
• Strong expertise in Agentic AI Knowledge Graphs GraphRAG AI-enabled document generation cloud infrastructure CI/CD and observability.
  
• Experience with MCP (Model Context Protocol) and/or A2A (Agent-to-Agent) interfaces is highly desirable.
  

• Design and build long-running multi-stage agentic workflows that can pause resume and recover reliably.
  
• Orchestrate LLMs and tools for retrieval calculations rules and structured extraction.
  
• Engineer prompts and structured-output contracts to produce validated machine-readable results.
  
• Build event-sourced workflows using frameworks such as Akka SDK with persistent state decisions and evidence.
  
• Design and maintain knowledge graphs containing entities relationships and source provenance.
  
• Develop ingestion and extraction pipelines that transform source documents into validated queryable graph structures.
  
• Implement GraphRAG vector retrieval and embeddings to provide grounded AI responses.
  
• Develop MCP tool servers and A2A interfaces and integrate with MCP-compatible clients and platforms such as Microsoft Copilot Studio.
  
• Establish grounding evaluation and safety standards including anti-hallucination controls regression testing edge-case testing and human-in-the-loop reviews.
  
• Implement guardrails for prompt injection data minimization and safe AI output generation.
  
• Own the complete lifecycle from development through production deployment and operations.
  
• Build and maintain CI/CD pipelines environment configurations secrets management and access controls.
  
• Work with enterprise AI services including managed model endpoints search/retrieval services document storage OCR/document intelligence and graph databases.
  
• Implement logging metrics tracing monitoring and alerting for production AI systems.
  
• Collaborate with engineering business and technology stakeholders to deliver secure explainable reliable and business-ready AI solutions.