Supports critical production services, monitoring, incident response, deployment validation, observability, and reliability improvements. Develops scripts, automation, AI-powered operational tools, runbooks, and workflows to reduce operational toil. Partners with development teams on reliability best practices, operational readiness, and production system understanding while learning SRE, distributed systems, and cloud operations under mentorship.
About the Role:
As a Junior Site Reliability Engineer, you will learn how modern software systems are built, operated, and improved in production.
You will work alongside experienced SREs who directly operate a small number of critical production services while also helping engineering teams improve reliability, observability, automation, and operational excellence.
This role is designed for engineers who enjoy software development, automation, troubleshooting, and understanding how complex distributed systems behave under real-world conditions.
You will gain hands on experience building and using AI powered operational tooling that helps engineers investigate incidents, improve observability, automate repetitive tasks, and eliminate operational toil.
Key Responsibilities:
Production Engineering
- Support the operation of critical services owned by the SRE team.
- Assist in monitoring service health, availability, and performance.
- Participate in incident response activities under mentorship.
- Learn on-call practices and production operations.
- Assist with deployment validation and operational readiness activities.
Reliability Engineering & Observability
- Support implementation of monitoring, alerting, dashboards, and observability improvements.
- Learn how logs, metrics, traces, and events are used to diagnose production environments.
- Help identify monitoring gaps and areas for improved visibility.
- Participate in post incident reviews and reliability improvement initiatives.
- Develop foundational understanding of SLIs, SLOs, and Error Budgets.
Automation & AI Operations
- Develop scripts and automation to eliminate repetitive operational work.
- Contribute to internally developed AI powered operational tools.
- Learn how AI can accelerate troubleshooting and root cause analysis.
- Support development of AI assisted runbooks and operational workflows.
- Help identify opportunities to reduce toil through software engineering and automation.
Engineering Enablement
- Partner with development teams to better understand production systems.
- Assist teams in adopting reliability and observability best practices.
- Support operational readiness reviews.
- Learn how reliability influences software design and architecture decisions.
Required Skills and Qualifications:
- 0-2 years of experience through employment, internship, or co-op programs.
- Degree or diploma in Computer Science, Software Engineering, or equivalent experience.
- Basic programming or scripting experience.
- Strong problem solving skills.
- Curiosity and desire to learn complex systems.
- Effective communication and teamwork skills.
Preferred Qualifications:
- Exposure to Azure cloud services.
- Exposure to Kubernetes or containerized applications.
- Familiarity with observability platforms such as Datadog, AppDynamics, Grafana, Prometheus, or ELK.
- Interest in AI, automation, and distributed systems.
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What you need to know about the Montreal Tech Scene
With roots dating back to 1642, Montreal is often recognized for its French-inspired architecture and cobblestone streets lined with traditional shops and cafés. But what truly sets the city apart is how it blends its rich tradition with a modern edge, reflected in its evolving skyline and fast-growing tech industry. According to economic promotion agency Montréal International, the city ranks among the top in North America to invest in artificial intelligence, making it le spot idéal for job seekers who want the best of both worlds.
Key Facts About Montreal Tech
- Number of Tech Workers: 255,000+ (2024, Tourisme Montréal)
- Major Tech Employers: SAP, Google, Microsoft, Cisco
- Key Industries: Artificial intelligence, machine learning, cybersecurity, cloud computing, web development
- Funding Landscape: $1.47 billion in venture capital funding in 2024 (BetaKit)
- Notable Investors: CIBC Innovation Banking, BDC Capital, Investissement Québec, Fonds de solidarité FTQ
- Research Centers and Universities: McGill University, Université de Montréal, Concordia University, Mila Quebec, ÉTS Montréal
