Job Description
Salary Range: $130,000 - $160,000 CAD
Location: Montreal, 3 days on site in Mile End office, 2 days remote
Introduction
Are you a systems-minded professional who thrives at the intersection of machine learning and infrastructure?
We're seeking an MLOps Engineer to design, build, and operate scalable machine learning pipelines and deployment workflows. Your role will be crucial in enabling fast, reliable, and automated ML-based service releases—helping teams transition smoothly from experimentation to production.
About Korbit:
Korbit is an AI-powered platform that automates code reviews, identifies bugs and vulnerabilities, and provides actionable guidance. It helps teams deliver high-quality code faster and upskills engineers through real-time, contextual feedback.
Founded by researchers from Mila Research Lab (Quebec, Canada) and Cambridge University (UK).
The Role
Reporting to the VP of Machine Learning, our MLOps Engineer will work 2 days remote, 3 days on-site. Your core responsibilities include:
Core Responsibilities
- End-to-End LLM Workflows
- Prototype, solidify, and productize LLM-centric features with the ML team.
- Embed automated tests and validations at every stage.
- Data-Ops Tooling
- Create and refine internal tools for dataset annotation, dynamic expansion, and prompt evaluation.
- Integrate data triggers into CI/CD for re-evaluation workflows.
- Prompt/Component Versioning & Drift Detection
- Establish a schema for tracking prompt revisions and LLM artifacts.
- Deploy automated checks for deviations.
- Observability & Incident Management
- Manage on-call duties, create runbooks, escalation paths, and post-mortems.
- Enhance dashboards and playbooks to reduce MTTD/MTTR.
Additional Tasks
- CI/CD Automation: Build GitHub Actions pipelines and AWS CodeDeploy hooks for validation and zero-downtime releases.
- Infrastructure as Code: Use Terraform modules for managing ECS, EC2, and other services.
- Real-Time Monitoring & SLA: Instrument systems with tools like BetterStack, Datadog, Sentry for uptime and latency targets.
- Collaboration: Document best practices, share pipeline health metrics, incident learnings, and process improvements.
Qualifications
- 3+ years experience in MLOps or DevOps roles focused on ML workloads.
- Proven expertise with LLM prompting and tools like LlamaIndex.
- Strong collaboration and documentation skills.
- Familiar with monitoring/alerting tools and ML governance.
To Apply
Send your CV and cover letter to ga@korbit.ai with the subject:
"MLOps Engineer Application"
Korbit is an equal-opportunity employer committed to diversity and inclusion.