Senior AI Engineer

Tekgence Inc; Toronto; Hybrid; CAD 140,000 - 210,000; Full time

Job summary

Tekgence Inc. in Toronto is seeking a senior software engineer specializing in GenAI, JVM, and graph technology. You will design, build, and operate production systems that leverage knowledge graphs, vector search, and AI patterns.

The role requires hands-on delivery of AI/LLM projects, strong Java/Kotlin/Scala skills, and ability to own end-to-end deployment in a hybrid work setup.

Qualifications

• 6-10 years building production software, including recent hands-on delivery of AI/LLM systems beyond prototypes.
• Strong JVM engineering with Git workflows, code review, automated testing, structured logging, and clean design.
• Hands-on experience with GenAI patterns: prompt engineering, structured or JSON outputs, tool 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: taking something built through a production pipeline and operating it.

Skills

Knowledge Graphs

GenAI patterns

JVM engineering

Graph databases

Production delivery

Tools

Neo4j

Cypher

GraphRAG

MongoDB Atlas

Job description

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.