Senior AI Engineer

Work Schedule: Hybrid, Tuesday to Thursday, 8:30 AM to 5:00 PM EST (3 days per week required in office)

Mandatory Skills

- Knowledge Graphs (Neo4j, Cypher, GraphRAG)

Required Qualifications

These apply to everyone we will consider.

• 6-10 years building production software, including recent hands-on delivery of AI or large-language-model systems beyond prototypes.
• Strong JVM engineering (Java; Kotlin or Scala a plus) with solid practices: Git workflows, code review, automated testing, structured logging, and clean design.
• Hands-on experience with modern GenAI patterns: prompt engineering, structured or JSON outputs, tool and function calling, retrieval-augmented generation, and agentic workflows.
• Experience designing and querying a graph or document database (Neo4j and Cypher, or MongoDB Atlas) and using vector search and embeddings for semantic retrieval.
• A track record of owning your own delivery path: you have taken something you built through a pipeline into production and operated it, rather than handing it over.
• Practical experience evaluating non-deterministic systems: test design, quality scoring, regression suites, and translating evaluation into business-ready acceptance criteria.
• Demonstrated ability to design and explain solution architecture (data flow, runtime flow, interfaces, failure modes, and controls) and to explain model behaviour, limitations, and trade-offs in plain language.

And real strength in one of these two adjacent areas

• Platform and reliability: a major cloud (Azure preferred), containerized deployment with Docker and Kubernetes, CI/CD, observability, and automated quality gates on a service you ran in production.
• Deliverable generation and rendering: producing Word, PowerPoint, PDF, or Excel output programmatically with libraries such as Apache POI, PDFBox, or pptxgenjs, making that output deterministic and testable, and moving comfortably between a JVM service and a Node.js rendering toolchain.

Preferred Qualifications

• Experience with the Model Context Protocol (MCP), building tool or resource servers and clients, and with agent-to-agent (A2A) interoperability.
• Experience integrating with low-code agent platforms such as Microsoft Copilot Studio.
• Experience with event-sourced or workflow frameworks (for example, the Akka SDK, Temporal, or similar) for long-running, restart-safe processes.
• Experience with cloud AI services (for example, Azure OpenAI or Azure AI, or equivalent), GraphRAG, and document-intelligence or OCR pipelines.
• Experience building conformance, golden-output, or contract-test harnesses.
• Comfort across languages: Python and Bash for tooling, and the ability to read a Node.js codebase as readily as a JVM one.
• Familiarity with Office Open XML internals, or with rendering diagrams and charts programmatically (SVG, layout engines such as elkjs, or headless rendering).
• Experience implementing GenAI guardrails and delivering under formal AI or model-risk governance.