Senior AI Engineer

Original job posting

The Senior AI Engineer will design and deliver production-grade AI and large-language-model systems, including agentic workflows and semantic retrieval pipelines. They will also manage the full lifecycle of these systems, from development and CI/CD deployment to observability and performance evaluation.

Job details

Role: Senior AI Engineer Toronto, ON Work Schedule: Hybrid, Tuesday to Thursday, 8:30 AM to 5:00 PM EST (3 days per week required in office) Mandatory Skills: - JVM Engineering (Java/Kotlin) - Knowledge Graphs (Neo4j, Cypher, GraphRAG) - MCP (Model Context Protocol) - Production Platform Engineering (CI/CD, Docker, Kubernetes, Observability) - Document Generation & Rendering (Apache POI, PDFBox, pptxgenjs, Office Open XML, Word/PPT/PDF generation, template management) 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.

What you’ll do

The Senior AI Engineer will design and deliver production-grade AI and large-language-model systems, including agentic workflows and semantic retrieval pipelines. They will also manage the full lifecycle of these systems, from development and CI/CD deployment to observability and performance evaluation.

Requirements

Candidates must have 6-10 years of experience in production software engineering with strong proficiency in JVM languages like Java or Kotlin. Additionally, they require hands-on experience with GenAI patterns, graph databases, and modern platform engineering practices.

Listed skills

• Microsoft Azure · Preferred
• Kubernetes · Preferred
• CI/CD · Preferred
• Docker · Preferred
• Kotlin · Preferred
• Java · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Java
• Kotlin
• Neo4j
• Cypher
• GraphRAG
• Model Context Protocol
• Docker
• Kubernetes
• CI/CD
• Observability
• Apache POI
• PDFBox
• Prompt Engineering
• Retrieval-Augmented Generation
• Agentic Workflows
• Azure

Job areas

• Technology
• Software
• Engineering
• Data & Analytics
• Consulting