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Added 14 mins ago
Senior Engineer, Engineering Governance
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Full-time
…requirement and the ability to sit at a computer for long periods of…
Added 14 mins ago
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LER TechForce

Technical Project Manager

Ottawa, ON, Canada
Kanata, ON, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Original job description

WHAT YOU WILL BE DOING

At Our Client´s Motor Company, it is believed freedom of movement drives human progress. With their exciting plans for the future of mobility, they have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow´s transportation. Modern vehicles are increasingly software-defined, connected, and intelligent. Delivering a best-in-class ownership and service experience now depends on Our Client´s ability to detect, understand, diagnose, and resolve complex software and electronics issues quickly and accurately. That is why Our Client is investing in an End-to-End Software Diagnostics & Observability initiative focused on transforming how vehicle issues are understood across engineering, diagnostics, and service workflows. We are building state-of-the-art AI-powered Embedded Vehicle Diagnostics capabilities that combine vehicle signals, diagnostics, logs, engineering knowledge, service procedures, and intelligent reasoning to improve case quality, accelerate fault isolation, guide next-best actions, and support scalable human-in-the-loop escalation. This initiative sits at the intersection of embedded systems, cloud services, diagnostics, observability, and AI/ML engineering. Do you want to help define the future of AI-enabled diagnostics for next-generation vehicles?

Our Client´s team is a fast-paced, highly collaborative organization that translates advanced technical strategy into deployable capabilities. If you are passionate about AI/ML, complex systems, embedded software, and solving real-world engineering problems at scale, consider joining our forward-thinking team. As a Systems Engineer - End to End Software Diagnostics & Observability, you will work with a cross-functional team responsible for defining, integrating, and maturing intelligent diagnostic workflows that span embedded vehicle behavior, cloud-based observability, AI reasoning engines, and human support processes. This role is ideal for a highly capable recent graduate or early-career engineer from a top engineering, computer science, or AI/ML program who wants to work on real-world AI systems for software-defined vehicles.

Responsibilities include but are not limited to:

Help define system-level requirements, interfaces, and workflows for Our Client´s End to End Software Diagnostics & Observability initiative.
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.
Help 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 design of systems that combine Our Client´s 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.
Work with internal teams and suppliers to integrate containerized AI solutions into Our Client´s-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 tradeoffs, risks, and recommendations clearly to engineering teams, product teams, and leadership.
Work under the guidance of experienced engineers.

WHAT YOU'LL NEED

Essential:

Systems Development Life Cycle, Software Systems, Systems Engineering, Product Management, Systems Architecture, Systems Analyst.
Software Systems - Understand how an application´s components work together, and help troubleshoot issues across the system.
Systems Analyst - Gather and document user or business requirements, analyze current processes, and translate needs into functional specifications.
Systems Architecture - Understand or help document how systems and components fit together, including integrations, data flows, and technology choices.
Systems Development Life Cycle - Participate in the stages of system delivery, from requirements and design through development, testing, deployment, and maintenance.
Systems Engineering - Help define system requirements and ensure components work together to meet performance, reliability, and operational needs.
Product Management - Help prioritize features, maintain a product backlog or roadmap, and coordinate with stakeholders and development teams.
Bachelor´s or Master´s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, Data Science, or related field.
3-6 years of experience in AI/ML engineering, embedded software, systems engineering, cloud engineering, or related areas through internships, research, academic projects, or full-time work.
Strong academic foundation in AI/ML engineering with practical familiarity in machine learning, LLMs, retrieval workflows, inference systems, model evaluation, and data pipelines.
Strong proficiency in Python.
Familiarity with AI/ML prototyping and engineering workflows, including training, inference, prompt-based systems, retrieval-augmented workflows, embeddings, ranking, or reasoning pipelines.
Familiarity with software engineering fundamentals, APIs, Git-based development, 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 system behavior.
Strong written and verbal communication skills and requirements authoring with 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.
4 days per week in office.

Preferred:

Python, GCP, Java, Artificial Intelligence & Expert Systems.
Artificial Intelligence & Expert Systems - Understand or contribute to a basic AI or rules-based feature, such as classifying requests or recommending a next step, while recognizing when human review is needed.
GCP - Use Google Cloud services to deploy or support an application, store data, or monitor a cloud-based system.
Python - Write or maintain basic Python scripts for automation, data processing, or application functionality.
Java - Write or maintain Java code for application features, business logic, and error handling.
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 PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, BigQuery, or similar AI/ML and cloud tools.
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, or diagnostic workflows.
Familiarity with GCP, Docker, GitHub, CI/CD, and observability tools such as Dynatrace or Grafana.
Experience through internships, research, or projects involving distributed systems, platform integration, or cloud-native services.
Ability to work well in a collaborative, agile environment with software, embedded, cloud, AI, and product teams.
Strong curiosity, ownership mindset, and willingness to learn quickly in a technically demanding domain.
Ability to be detail oriented while understanding broader system and product goals.

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More about this job

Responsibilities

Define and refine system requirements, interfaces, and workflows for AI-powered vehicle diagnostics and observability across embedded, cloud, data, and AI/ML systems. Support integration, validation, deployment, issue triage, and cross-functional problem solving for diagnostic capabilities and workflows.

Requirements

A bachelor's or master's degree in a relevant engineering, computer science, AI/ML, or related field is required, along with 3–6 years of relevant experience through work, internships, research, or projects. Candidates should have strong Python and AI/ML foundations, familiarity with software engineering and cloud workflows, and the ability to translate ambiguous needs into technical requirements and communicate effectively.

Skills

Systems Engineering
Systems Architecture
Requirements Analysis
Product Management
Python
Artificial Intelligence
Machine Learning
Large Language Models
Retrieval-Augmented Generation
Embedded Systems
Vehicle Diagnostics
Cloud Engineering
Google Cloud Platform
APIs
Observability

Visa sponsorship

Not detected in the job text

Keywords

Systems Engineering
Systems Architecture
Requirements Analysis
Product Management
Python
Java
Artificial Intelligence
Machine Learning
Large Language Models
Retrieval-Augmented Generation
Semantic Retrieval
Vector Search
PyTorch
TensorFlow
Scikit-Learn
LangChain
Vertex AI
Google Cloud Platform
BigQuery
Docker
Git
CI/CD
APIs
Embedded Software
Vehicle Diagnostics
Diagnostic Trouble Codes
Observability
Grafana
Dynatrace

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