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, 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 h

📌 AI/ML Systems Engineer (Ahuntsic)
🏢 Actalent
📍 Ahuntsic