AI Engineer

Role overview A mid-level engineering role focused on building and shipping agentic AI features that integrate with enterprise systems, working alongside senior engineers and domain experts. The position centers on production LLM and agent pipelines — retrieval, tool use, prompt design, and evaluation — connected to enterprise platforms serving large organizations and public-sector clients across Canada.

Responsibilities - Implement and maintain production LLM and agent pipelines, including retrieval, tool use, prompt templates, and evaluation harnesses across client engagements. - Build integrations between AI services and enterprise platforms such as EAM, CMMS, ERP, and data warehouses via REST, GraphQL, or message-based APIs. - Contribute to prompt design, dataset curation, and offline/online evaluation to improve model quality and task success rates. - Collaborate with senior engineers on system design, code review, and observability for AI workloads. - Support clients during pilots by investigating edge cases, iterating on agent behaviour, and turning feedback into scoped improvements.

Requirements - 2+ years of professional software engineering experience, including hands-on work with LLMs, RAG pipelines, or ML models in production or near-production settings. - Strong Python skills and comfort with at least one of TypeScript/Node.js, Go, or Java for service development. - Practical experience with AI tooling such as OpenAI, Anthropic, or Azure OpenAI APIs, LangChain, LangGraph, LlamaIndex, or equivalents, plus vector stores. - Solid fundamentals in data structures, APIs, SQL, and testing, with code that others can safely extend. - Clear written and verbal communication with the ability to explain trade-offs to teammates and clients. - Must be legally authorized to work in Canada.

Nice to have - Exposure to public-sector or regulated-industry projects in government, municipalities, universities, or healthcare. - Familiarity with evaluation frameworks such as Ragas or DeepEval and LLM observability tools. - Experience with containerization, CI/CD, and cloud deployment on Azure, AWS, or GCP. - Open-source contributions or personal projects involving agents, RAG, or fine-tuning.

Benefits and work setup - Full-time mid-level role based in Edmonton or remote within Canada, with hybrid or remote work modes available.