Systems Engineer – End-to-End Software Diagnostics & Observability
Location: Ottawa, ON
Work Model: Hybrid (4 days/week onsite)
Employment Type: Contract
Partnered Through: Epitec
Overview
Epitec is seeking a Systems Engineer to support the development of next-generation software diagnostics and observability solutions for connected, intelligent vehicles. This role sits at the intersection of AI/ML, embedded systems, cloud technologies, and systems engineering, helping to build innovative diagnostic capabilities that improve issue detection, root cause analysis, and service outcomes.
This is an excellent opportunity for an early-career engineer with strong AI/ML and software systems experience who is passionate about solving complex technical challenges in a collaborative, fast-paced environment.
Key Responsibilities
- Define and refine system requirements, interfaces, and workflows for diagnostic and observability platforms.
- Support the development of AI-enabled diagnostic solutions that improve issue detection, troubleshooting, and guided repair processes.
- Collaborate across embedded software, cloud, data, and AI/ML teams to integrate intelligent diagnostic capabilities.
- Translate business, service, and engineering needs into technical requirements and functional specifications.
- Support AI-driven features including knowledge retrieval, diagnostic reasoning, decision support, orchestration, and validation workflows.
- Define requirements for diagnostic evidence collection, including logs, traces, fault codes, telemetry, and system events.
- Participate in system integration, issue triage, root cause analysis, and cross-functional problem solving.
- Help establish observability requirements, including metrics, dashboards, alerts, and escalation workflows.
- Evaluate AI system performance for accuracy, traceability, and effectiveness using diagnostic and service data.
- Communicate technical recommendations, risks, and tradeoffs to stakeholders and leadership.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- 3 to 6 years of experience in AI/ML engineering, systems engineering, embedded software, cloud engineering, or related technical disciplines.
- Strong proficiency in Python.
- Knowledge of machine learning, large language models (LLMs), inference systems, model evaluation, and data pipelines.
- Familiarity with retrieval-augmented generation (RAG), embeddings, ranking systems, prompt-based AI solutions, or reasoning pipelines.
- Understanding of software engineering fundamentals, APIs, Git-based development, and containerized applications.
- Ability to gather requirements, analyze business needs, and translate them into functional specifications.
- Strong written and verbal communication skills.
- Experience building or prototyping AI-enabled systems through work, research, internships, or academic projects.
Preferred Qualifications
- Experience with AI/ML frameworks and tools such as PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, or BigQuery.
- Experience developing chatbots, copilots, intelligent assistants, search platforms, or decision-support systems.
- Familiarity with evaluation methodologies for AI grounding, explainability, confidence scoring, and governance.
- Exposure to embedded software, connected systems, diagnostics, or intelligent support workflows.
- Experience with Google Cloud Platform (GCP), Docker, GitHub, CI/CD pipelines, and observability platforms.
- Knowledge of Java development.
- Experience working with distributed systems, platform integrations, or cloud-native services.
- Strong problem-solving abilities, attention to detail, and a collaborative mindset.
Core Skills
Required
- Systems Engineering
- Systems Architecture
- Systems Analysis
- Systems Development Life Cycle (SDLC)
- Software Systems
- Product Management
Preferred
- Python
- GCP
- Java
- Artificial Intelligence / Expert Systems
Work Environment
- Hybrid schedule requiring onsite presence 4 days per week.
- Candidates must be able to work onsite as required from day one.
- Collaborative environment working alongside software, cloud, AI/ML, embedded systems, and product teams.
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