Job Title: Systems Engineer AI/ML Diagnostics & Observability
Job Description
This role focuses on building end-to-end software diagnostics and observability capabilities for software-defined vehicles, using state-of-the-art AI-powered embedded vehicle diagnostics and cloud-based observability. As a Systems Engineer, you will help define, integrate, and mature intelligent diagnostic workflows that connect embedded vehicle behavior, AI reasoning engines, cloud services, and human support processes.
Responsibilities
- Define system-level requirements, interfaces, and workflows for end-to-end software diagnostics and observability initiatives.
- Support development of AI-powered embedded vehicle diagnostics capabilities that improve issue detection, case intake quality, root-cause isolation, and guided repair.
- Work across embedded, cloud, data, and AI/ML domains to connect vehicle diagnostics with intelligent reasoning and observability workflows.
- Translate business, service, and engineering needs into technical requirements for diagnostic systems, AI engines, APIs, workflow orchestration, and support tooling.
- Support AI/ML-driven capabilities such as case intake assistance, knowledge retrieval, diagnostic reasoning, decision support, validation, and orchestration.
- Define and refine requirements for diagnostic evidence collection, including DTCs, PIDs, Freeze Frame data, logs, event traces, module state, and procedural outcomes.
- Support the design of systems that combine engineering knowledge, diagnostics data, and AI reasoning to isolate likely root causes in embedded vehicle systems.
- Participate in evaluation and validation of AI system behavior using diagnostic evidence, service data, engineering content, and observability signals.
- Collaborate with internal teams and suppliers to integrate containerized AI solutions into managed cloud environments and workflow systems.
- Help define observability requirements for diagnostic systems, including logs, metrics, traces, dashboards, alerts, and escalation workflows.
- Participate in system integration, issue triage, root-cause analysis, and cross-functional technical problem solving.
- Support rapid iteration, testing, and deployment of AI-enabled diagnostic capabilities into engineering and non-production environments.
- Communicate technical trade-offs, risks, and recommendations clearly to engineering teams, product teams, and leadership.
- Contribute to product management activities by helping prioritize features, maintain product backlogs or roadmaps, and coordinate with stakeholders and development teams.
- Engage in the full systems development life cycle from requirements and design through development, testing, deployment, and maintenance.
Essential Skills
- Bachelors or Masters degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, Data Science, or a related field.
- 36 years of experience in AI/ML engineering, embedded software, systems engineering, cloud engineering, or closely related areas gained through internships, research, academic projects, or full-time work.
- 36 years of systems engineering experience, including requirements definition, system integration, and diagnostics workflows.
- 36 years of AI/ML experience, including machine learning, large language models (LLMs), retrieval workflows, inference systems, model evaluation, and data pipelines.
- 36 years of software engineering experience with APIs, Git-based development, and containerized application workflows.
- Strong academic foundation in AI/ML engineering with practical familiarity in machine learning, LLMs, retrieval workflows, inference systems, model evaluation, and data pipelines.
- Proficiency in Python for automation, data processing, and application functionality.
- Familiarity with AI/ML prototyping and engineering workflows, including training, inference, prompt-based systems, retrieval-augmented workflows, embeddings, ranking, and reasoning pipelines.
- Familiarity with software engineering fundamentals, APIs, Git, and containerized application workflows.
- Interest in embedded systems, vehicle diagnostics, software-defined vehicles, and intelligent support workflows.
- Ability to translate ambiguous problem statements into structured technical requirements and clear system behavior.
- Strong written and verbal communication skills, including requirements authoring and the ability to drive high-level, abstract conversations with leadership.
- Demonstrated ability through coursework, research, internships, or projects to build or prototype AI/ML-enabled systems.
- Understanding of software systems and how application components work together, with the ability to help troubleshoot issues across the system.
- Experience gathering and documenting user or business requirements, analyzing current processes, and translating needs into functional specifications.
- Ability to understand and help document systems architecture, including integrations, data flows, and technology choices.
- Experience participating in the systems development life cycle from requirements and design through development, testing, deployment, and maintenance.
- Systems engineering skills to define system requirements and ensure components work together to meet performance, reliability, and operational needs.
- Product management skills to help prioritize features, maintain product backlogs or roadmaps, and coordinate with stakeholders and development teams.
Additional Skills & Qualifications
- Education from a highly regarded engineering, computer science, or AI/ML program with strong evidence of technical rigor.
- Hands-on experience with LLMs, semantic retrieval, vector search, ranking systems, agent-based workflows, or decision-support systems.
- Experience with AI/ML and cloud tools such as PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, and BigQuery.
- Experience building chatbots, copilots, AI assistants, search systems, or reasoning systems.
- Familiarity with evaluation of AI systems for grounding, confidence, traceability, explainability, and policy compliance.
- Familiarity with embedded software systems, electronic control modules, diagnostics, or connected vehicle technologies.
- Exposure to DTCs, PIDs, Freeze Frame data, logs, vehicle network data, and diagnostic workflows.
- Familiarity with Google Cloud Platform (GCP), Docker, GitHub, CI/CD pipelines, and observability tools such as Dynatrace or Grafana.
- Experience with distributed systems, platform integration, or cloud-native services through internships, research, or projects.
- Ability to work effectively in a collaborative, agile environment with software, embedded, cloud, AI, and product teams.
- Curiosity, strong ownership mindset, and willingness to learn quickly in a technically demanding domain.
- Ability to be detail-oriented while maintaining an understanding of broader system and product goals.
- Knowledge of artificial intelligence and expert systems, including contributing to AI or rules-based features such as request classification or next-step recommendations while recognizing when human review is needed.
- Ability to use Google Cloud services to deploy or support applications, store data, or monitor cloud-based systems.
- Experience writing or maintaining Python scripts for automation, data processing, or application functionality.
- Familiarity with Java for application features, business logic, and error handling.
- Experience with observability practices and tools in cloud-native environments.
Work Environment
This is a hybrid contract position based at an engineering center in Kanata, Ontario. The role requires onsite presence four days per week from day one, and remote-only arrangements are not available. Candidates who relocate are responsible for their own relocation expenses. The position follows a standard daytime schedule from 8:00 a.m. to 5:00 p.m., with no anticipated travel requirements. You will work in a fast-paced environment that emphasizes collaboration across embedded software, cloud, AI/ML, diagnostics, engineering, and product teams to deliver AI-enabled diagnostic and observability solutions for software-defined vehicles. The work involves modern cloud-native technologies, containerized workflows, observability tooling, and enterprise-scale engineering platforms, providing meaningful ownership, exposure to emerging AI technologies, and participation in highly visible innovation initiatives.
Job Type & Location
This is a Contract position based out of Ottawa, ON.
Pay And Benefits
The pay range for this position is $55.00 - $79.00/hr.
Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors.
Job Type & Location
This is a Contract position based out of Ottawa, ON.
Workplace Type
This is a hybrid position in Ottawa,ON.
À propos d'Actalent
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About Actalent
Actalent is a global leader in engineering and sciences services and talent solutions. We help visionary companies advance their engineering and science initiatives through access to specialized experts who drive scale, innovation and speed to market. With a network of almost 20,000 consultants and 5,000 clients across the U.S., Canada, Asia and Europe, Actalent serves many of the Fortune 500. We are proud to be an Engineering News-Record (ENR) Top 500 Design Firm for our engineering design services and a ClearlyRated Best of Staffing® winner for both client and talent service.
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