AI/ML Systems Engineer

Role overview

Responsibilities

• Define system requirements, interfaces, workflows, and technical requirements for diagnostics, AI engines, APIs, orchestration, and support tools.
• Support AI-enabled issue detection, case intake, root-cause isolation, guided repair, knowledge retrieval, decision support, validation, and orchestration.
• Define diagnostic evidence needs, including DTCs, PIDs, Freeze Frame data, logs, event traces, module state, and procedural outcomes.
• Evaluate AI behavior and help combine engineering knowledge, diagnostic data, and AI reasoning to identify likely vehicle-system root causes.
• Integrate containerized AI solutions into managed cloud environments; define logs, metrics, traces, dashboards, alerts, and escalation requirements.
• Participate in system integration, issue triage, root-cause analysis, testing, deployment, and the systems development life cycle.
• Help prioritize features, maintain backlogs or roadmaps, coordinate stakeholders, and communicate technical trade-offs.

Essential skills and qualifications

• Bachelor’s or master’s degree in computer science, computer or electrical engineering, systems engineering, AI/ML, robotics, data science, or a related field.
• The posting repeatedly specifies “36 years” of experience in related AI/ML, systems, and software engineering areas; this duration is reproduced as written and may be a source typo.
• Python proficiency; experience or familiarity with machine learning, LLMs, retrieval workflows, inference, model evaluation, data pipelines, APIs, Git, and containerized applications.
• Interest in embedded systems, vehicle diagnostics, software-defined vehicles, and intelligent support workflows.
• Ability to structure ambiguous problems, document requirements and architecture, communicate clearly, and work across the systems development life cycle.
• Ability to demonstrate AI/ML system prototyping through coursework, research, internships, or projects.
• Additional qualifications include LLMs, semantic or vector search, ranking, agent workflows, AI assistants, cloud tools, embedded diagnostics, and observability tools.

Work environment

• Hybrid contract role based at an engineering center in Kanata, Ontario; onsite presence is required four days per week from day one, and remote-only arrangements are unavailable.
• The work involves collaboration across embedded software, cloud, AI/ML, diagnostics, engineering, and product teams.
• Candidates relocating are responsible for their own relocation expenses.

Pay

• Pay range is CAD 55.00–79.00 per hour; individual compensation depends on qualifications, skills, experience, job knowledge, location, and other job-related factors.

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