AI/ML Systems Engineer

Role overview

Systems and diagnostics responsibilities

• Define system requirements, interfaces, and workflows for software diagnostics and observability initiatives.
• Support AI-powered embedded diagnostics for issue detection, case intake, root-cause isolation, and guided repair.
• Translate business, service, and engineering needs into requirements for diagnostic systems, AI engines, APIs, workflow orchestration, and support tools.
• Define evidence collection requirements, including DTCs, PIDs, Freeze Frame data, logs, event traces, module state, and procedural outcomes.
• Help combine engineering knowledge, diagnostics data, and AI reasoning to isolate likely embedded vehicle root causes.
• Evaluate AI behavior using diagnostic evidence, service data, engineering content, and observability signals.

Integration, delivery, and product work

• Connect embedded, cloud, data, and AI/ML domains; collaborate with teams and suppliers to integrate containerized AI solutions into managed cloud workflows.
• Define observability requirements for logs, metrics, traces, dashboards, alerts, and escalation workflows.
• Participate in integration, triage, root-cause analysis, testing, and rapid deployment to engineering and non-production environments.
• Contribute to product management by prioritizing features, maintaining backlogs or roadmaps, and coordinating stakeholders and development teams.
• Participate in the full systems development life cycle from requirements and design through development, testing, deployment, and maintenance.

Essential qualifications

• Bachelor's or master's degree in computer science, computer engineering, electrical engineering, systems engineering, AI, machine learning, robotics, data science, or a related field.
• The posting repeatedly says '36 years' of relevant experience; the intended experience range is ambiguous.
• Experience areas include systems engineering, requirements definition, integration, diagnostics workflows, AI/ML, LLMs, retrieval, inference, model evaluation, data pipelines, APIs, Git, and containerized applications.
• Strong AI/ML foundation; Python proficiency for automation, data processing, and application functionality.
• Familiarity with training, inference, prompt-based systems, retrieval-augmented workflows, embeddings, ranking, and reasoning pipelines.
• Ability to translate ambiguous problems into requirements, troubleshoot systems, document architecture, and understand integrations and data flows.
• Experience or demonstrated project work across the systems development life cycle; strong written and verbal communication.

Additional skills and qualifications

• Additional skills are preferred or described as familiarity, exposure, or experience rather than essential requirements.
• Hands-on LLMs, semantic retrieval, vector search, ranking, agent workflows, or decision-support systems; chatbot, copilot, AI assistant, search, or reasoning-system development.
• AI/ML and cloud tools may include PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, and BigQuery.
• AI evaluation familiarity may include grounding, confidence, traceability, explainability, and policy compliance.
• Embedded software, electronic control modules, connected vehicles, DTCs, PIDs, Freeze Frame data, vehicle networks, and diagnostic workflows.
• GCP, Docker, GitHub, CI/CD, Dynatrace, Grafana, distributed systems, cloud-native integration, and observability practices.
• Java familiarity for application features, business logic, and error handling; knowledge of AI or expert systems and human review needs.

Work environment

• Hybrid contract role based at an engineering center in Kanata, Ontario, with onsite presence four days per week from day one; remote-only arrangements are unavailable.
• Relocation expenses are the candidate's responsibility. No travel is anticipated.
• Standard daytime schedule is 8:00 a.m. to 5:00 p.m.
• Work involves collaboration across embedded software, cloud, AI/ML, diagnostics, engineering, and product teams.

Pay

• Pay range is CAD 55 to CAD 79 per hour; individual compensation depends on qualifications, skills, experience, job knowledge, location, and other job-related factors.

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