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Added 12 mins ago
Manager, Technology Services IMITS
Sanya’k̓ula
Vancouver, BC
Full-time
The Manager, has direct responsibility for the designated portfolio in support of a shared Technology Services solution within healthcare across the province including the supervision of…
Added 12 mins ago
Site Inspector
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$60,000 to $80,000 CAD / year
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Added 1 hour ago
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EPITEC

Systems Engineer - End-to-End Software Diagnostics & Observability

Ottawa, ON, Canada

Job type: Contract
Apply by: Oct 24, 2026
Experience: 3 to 6 years of experience in AI/ML engineering, systems engineering, embedded software, cloud engineering, or related technical disciplines.
Posting language: English
Posted: Sep 24, 2026

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Original job description

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.

#INDOEM

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More about this job

Responsibilities

Define system requirements, interfaces, and workflows for diagnostic and observability platforms, and support AI-enabled capabilities for issue detection, troubleshooting, and guided repairs. Collaborate across embedded, cloud, data, and AI/ML teams to integrate and evaluate solutions, establish observability requirements, and communicate technical recommendations and risks.

Requirements

Requires a bachelor's or master's degree in a relevant technical field and 3–6 years of experience in AI/ML engineering, systems engineering, embedded software, cloud engineering, or a related discipline. Candidates need strong Python skills, knowledge of machine learning and LLM systems, familiarity with RAG and software engineering fundamentals, and the ability to translate needs into specifications and communicate effectively.

Skills

Systems Engineering
Systems Architecture
Systems Analysis
Python
Artificial Intelligence
Machine Learning
Large Language Models
Retrieval-Augmented Generation
Cloud Engineering
Embedded Software
Requirements Analysis
Observability
Root Cause Analysis
Google Cloud Platform
Java

Visa sponsorship

Not detected in the job text

Keywords

Systems Engineering
Systems Architecture
Systems Analysis
Python
Machine Learning
Artificial Intelligence
Large Language Models
Inference Systems
Model Evaluation
Data Pipelines
Retrieval-Augmented Generation
Embeddings
Ranking Systems
Prompt-Based AI
Reasoning Pipelines
APIs
Git
Containerized Applications
PyTorch
TensorFlow
Scikit-Learn
LangChain
Vertex AI
BigQuery
Google Cloud Platform
Docker
GitHub
CI/CD
Observability

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