Our Client is seeking two Systems Engineers – End-to-End Software Diagnostics & Observability in Kanata, ON to join their software-defined vehicle organization. Operating on a Hybrid schedule (4 days per week in office), these roles sit at the intersection of embedded vehicle systems, cloud services, platform observability, and AI/ML engineering. We are recruiting for two openings that accommodate different levels of tenure and autonomy:
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Systems Engineer (Autonomous / Intermediate) – Focuses on driving system architecture, abstract requirements, and cross-functional technical conversations with leadership.
Systems Engineer (Mentored / Early-Career) – Focuses on SDLC execution, diagnostic evidence analysis, and feature integration under the direct guidance of senior engineers.
In these positions, you will act as a systems engineering bridge across embedded software, cloud infrastructure, data science, and AI/ML domain teams. Modern vehicles are software-defined, connected, and intelligent; delivering best-in-class service relies on real-time fault detection, structured evidence capture, and automated reasoning engines. You will translate complex vehicle signals, service procedures, and business goals into structured system requirements, API contracts, data flow architectures, and observability guidelines.
Whether you are an established systems engineer looking to lead cross-domain architecture or a top-tier graduate/early-career engineer seeking structured mentorship, this initiative offers direct experience building real-world AI diagnostic tools (copilots, RAG pipelines, reasoning engines) for next-generation vehicle architectures.
Advantages
Next-Gen Automotive AI Scope: Build real-world AI/ML diagnostic and observability capabilities for connected vehicle fleets.
End-to-End Cross-Domain Scope: Work across physical electronic control units (ECUs), cloud microservices, vector search engines, and human-in-the-loop support tools.
Flexible Level Alignment: Entry points tailored for both independent intermediate systems engineers and mentored early-career professionals.
Modern AI & Cloud Tech Stack: Gain practical experience with Python, GCP, Vertex AI, BigQuery, Docker, LangChain, and enterprise observability tools (Dynatrace, Grafana).
Responsibilities
1. Systems Architecture & Requirements Engineering
Define system-level requirements, data flows, and interface specifications for the End-to-End Software Diagnostics & Observability platform.
Translate business, service, and engineering goals into structured technical specifications, functional workflows, and API definitions.
Define and refine requirements for diagnostic evidence collection, including Diagnostic Trouble Codes (DTCs), Parameter IDs (PIDs), Freeze Frame data, system logs, event traces, and module state changes.
(Autonomous Role) Drive high-level abstract technical discussions and communicate tradeoffs, risks, and recommendations directly to engineering leadership and product teams.
2. AI Diagnostic Capabilities & Reasoning Workflows
Support the development and evaluation of AI-driven diagnostic capabilities (case intake assistance, knowledge retrieval, diagnostic reasoning, decision support, and escalation orchestration).
Architect workflows combining engineering knowledge, historical service data, and live vehicle diagnostics to isolate likely root causes in embedded systems.
Participate in validating AI system behavior using real-world diagnostic evidence, evaluating models for grounding, confidence, explainability, and policy compliance.
3. Cloud Integration, Observability & Triage
Define system observability requirements, including structured logs, metrics, traces, operational dashboards, alert rules, and escalation pathways.
Partner with internal squads and external suppliers to integrate containerized AI solutions into managed cloud environments and workflow systems.
Participate in cross-functional system integration, defect triage, root-cause analysis, and rapid testing of AI capabilities in non-production environments.
Qualifications
Foundational Requirements (Both Roles)
Education: Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, or Data Science.
Programming: Strong proficiency in Python for script automation, data processing, and application/AI prototyping.
AI/ML Technical Depth: Practical familiarity with AI/ML engineering workflows (LLMs, Retrieval-Augmented Generation [RAG], vector embeddings, ranking, prompt systems, or inference pipelines).
Software Engineering Basics: Familiarity with REST APIs, Git-based version control, containerized application workflows (Docker), and cloud computing platforms (GCP).
Level-Specific Qualifications
Role 1: Systems Engineer (Autonomous Level)
4–6 years of professional experience in systems engineering, AI/ML engineering, or embedded cloud systems.
Demonstrated track record authoring complex system specifications and architecting multi-component workflows.
Proven ability to lead cross-functional architecture reviews and present technical tradeoffs to executive stakeholders.
Role 2: Systems Engineer (Mentored Level)
3–5 years of relevant experience (full-time work, research, academic projects, or internships).
Strong foundation in the Systems Development Life Cycle (SDLC), systems analysis, and process modeling.
Eagerness to learn in a technically demanding environment while working under structured senior mentorship.
Preferred Qualifications (Nice to Have)
Practical exposure to AI/ML tools: PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, or BigQuery.
Familiarity with embedded systems, Electronic Control Units (ECUs), connected vehicle tech, or vehicle diagnostic concepts (DTCs, PIDs, Freeze Frame data, CAN bus).
Exposure to cloud-native observability platforms (Dynatrace, Grafana).
Summary
Our Client is seeking two Systems Engineers – End-to-End Software Diagnostics & Observability in Kanata, ON to join their software-defined vehicle organization. Operating on a Hybrid schedule (4 days per week in office), these roles sit at the intersection of embedded vehicle systems, cloud services, platform observability, and AI/ML engineering. We are recruiting for two openings that accommodate different levels of tenure and autonomy:
Systems Engineer (Autonomous / Intermediate) – Focuses on driving system architecture, abstract requirements, and cross-functional technical conversations with leadership.
Systems Engineer (Mentored / Early-Career) – Focuses on SDLC execution, diagnostic evidence analysis, and feature integration under the direct guidance of senior engineers.
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
Our Client is seeking two Systems Engineers – End-to-End Software Diagnostics & Observability in Kanata, ON to join their software-defined vehicle organization. Operating on a Hybrid schedule (4 days per week in office), these roles sit at the intersection of embedded vehicle systems, cloud services, platform observability, and AI/ML engineering. We are recruiting for two openings that accommodate different levels of tenure and autonomy:
Systems Engineer (Autonomous / Intermediate) – Focuses on driving system architecture, abstract requirements, and cross-functional technical conversations with leadership.
Systems Engineer (Mentored / Early-Career) – Focuses on SDLC execution, diagnostic evidence analysis, and feature integration under the direct guidance of senior engineers.
In these positions, you will act as a systems engineering bridge across embedded software, cloud infrastructure, data science, and AI/ML domain teams. Modern vehicles are software-defined, connected, and intelligent; delivering best-in-class service relies on real-time fault detection, structured evidence capture, and automated reasoning engines. You will translate complex vehicle signals, service procedures, and business goals into structured system requirements, API contracts, data flow architectures, and observability guidelines.
...
Whether you are an established systems engineer looking to lead cross-domain architecture or a top-tier graduate/early-career engineer seeking structured mentorship, this initiative offers direct experience building real-world AI diagnostic tools (copilots, RAG pipelines, reasoning engines) for next-generation vehicle architectures.
Advantages
Next-Gen Automotive AI Scope: Build real-world AI/ML diagnostic and observability capabilities for connected vehicle fleets.
End-to-End Cross-Domain Scope: Work across physical electronic control units (ECUs), cloud microservices, vector search engines, and human-in-the-loop support tools.
Flexible Level Alignment: Entry points tailored for both independent intermediate systems engineers and mentored early-career professionals.
Modern AI & Cloud Tech Stack: Gain practical experience with Python, GCP, Vertex AI, BigQuery, Docker, LangChain, and enterprise observability tools (Dynatrace, Grafana).
Responsibilities
1. Systems Architecture & Requirements Engineering
Define system-level requirements, data flows, and interface specifications for the End-to-End Software Diagnostics & Observability platform.
Translate business, service, and engineering goals into structured technical specifications, functional workflows, and API definitions.
Define and refine requirements for diagnostic evidence collection, including Diagnostic Trouble Codes (DTCs), Parameter IDs (PIDs), Freeze Frame data, system logs, event traces, and module state changes.
(Autonomous Role) Drive high-level abstract technical discussions and communicate tradeoffs, risks, and recommendations directly to engineering leadership and product teams.
2. AI Diagnostic Capabilities & Reasoning Workflows
Support the development and evaluation of AI-driven diagnostic capabilities (case intake assistance, knowledge retrieval, diagnostic reasoning, decision support, and escalation orchestration).
Architect workflows combining engineering knowledge, historical service data, and live vehicle diagnostics to isolate likely root causes in embedded systems.
Participate in validating AI system behavior using real-world diagnostic evidence, evaluating models for grounding, confidence, explainability, and policy compliance.
3. Cloud Integration, Observability & Triage
Define system observability requirements, including structured logs, metrics, traces, operational dashboards, alert rules, and escalation pathways.
Partner with internal squads and external suppliers to integrate containerized AI solutions into managed cloud environments and workflow systems.
Participate in cross-functional system integration, defect triage, root-cause analysis, and rapid testing of AI capabilities in non-production environments.
Qualifications
Foundational Requirements (Both Roles)
Education: Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, or Data Science.
Programming: Strong proficiency in Python for script automation, data processing, and application/AI prototyping.
AI/ML Technical Depth: Practical familiarity with AI/ML engineering workflows (LLMs, Retrieval-Augmented Generation [RAG], vector embeddings, ranking, prompt systems, or inference pipelines).
Software Engineering Basics: Familiarity with REST APIs, Git-based version control, containerized application workflows (Docker), and cloud computing platforms (GCP).
Level-Specific Qualifications
Role 1: Systems Engineer (Autonomous Level)
4–6 years of professional experience in systems engineering, AI/ML engineering, or embedded cloud systems.
Demonstrated track record authoring complex system specifications and architecting multi-component workflows.
Proven ability to lead cross-functional architecture reviews and present technical tradeoffs to executive stakeholders.
Role 2: Systems Engineer (Mentored Level)
3–5 years of relevant experience (full-time work, research, academic projects, or internships).
Strong foundation in the Systems Development Life Cycle (SDLC), systems analysis, and process modeling.
Eagerness to learn in a technically demanding environment while working under structured senior mentorship.
Preferred Qualifications (Nice to Have)
Practical exposure to AI/ML tools: PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, or BigQuery.
Familiarity with embedded systems, Electronic Control Units (ECUs), connected vehicle tech, or vehicle diagnostic concepts (DTCs, PIDs, Freeze Frame data, CAN bus).
Exposure to cloud-native observability platforms (Dynatrace, Grafana).
Summary
Our Client is seeking two Systems Engineers – End-to-End Software Diagnostics & Observability in Kanata, ON to join their software-defined vehicle organization. Operating on a Hybrid schedule (4 days per week in office), these roles sit at the intersection of embedded vehicle systems, cloud services, platform observability, and AI/ML engineering. We are recruiting for two openings that accommodate different levels of tenure and autonomy:
Systems Engineer (Autonomous / Intermediate) – Focuses on driving system architecture, abstract requirements, and cross-functional technical conversations with leadership.
Systems Engineer (Mentored / Early-Career) – Focuses on SDLC execution, diagnostic evidence analysis, and feature integration under the direct guidance of senior engineers.
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
