Senior AI Engineer

Toronto, ON
Hybrid
Posted Oct 14, 2026
1 position

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Employment type: Contract
Experience level: Senior · 5+ years
Posting language: English
Working hours: 40 hours per week
Office presence: 3 days per week
Seniority: Mid-Senior level
Application method: Apply on LinkedIn (LinkedIn sign-in required)

Job summary

Build and operate production AI and large-language-model systems, including graph- and document-based retrieval, agent workflows, and integrations. Design reliable platform or document-generation solutions, and evaluate their quality, failure modes, and controls for production use.

Job details

Job Title: Senior AI Engineer Job Location: Toronto,Ontario-3days per week in office Required-Hybrid Job Type:Long Term Contract 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

Requirements

Requires 6–10 years of production software experience, strong JVM engineering, hands-on delivery of AI/LLM systems, and experience with databases, embeddings, and retrieval. Candidates must have owned production delivery and be able to evaluate non-deterministic systems and explain solution architecture; strength in platform reliability or programmatic document generation is also required.