Senior Platform Engineer - ML Infrastructure (Winnipeg)

Job Overview

Join Team CARFAX as a Senior Platform Engineer – ML Infrastructure. We are looking for a seasoned Senior Platform Engineer to join our platform team and take an active role in designing, scaling, and operating the infrastructure that powers Large Language Model (LLM) development and hosting.

This is a high‑impact, highly technical position where you will own critical platform components, drive architectural decisions, and directly shape the reliability, performance, and security of our AI infrastructure. At its core, this is a Kubernetes‑first, cloud‑native platform engineering role. We care deeply about your ability to architect and operate scalable, resilient infrastructure for LLM workloads— the specific cloud or tooling background is secondary.

Our current platform runs on AWS with EKS, Flyte, ArgoCD, JupyterHub, and the LGTM observability stack, and you'll work within that environment.

We are looking for an engineer who thrives at the intersection of AI/ML and cloud‑native infrastructure, who gets excited about solving the unique scaling and operational challenges that LLM workloads demand, and who wants to work on technology that sits at the absolute cutting edge of the AI industry.

The position requires 2 days in the London, ON office per week. Our four‑day week continues in Summer 2026.

What You’ll Own

• LLM Platform Architecture – Actively participate in the design and evolution of the core infrastructure platform supporting LLM training, fine‑tuning, and inference workloads at scale.
• Kubernetes & Advanced Autoscaling – Own the design and implementation of sophisticated K8s autoscaling strategies (HPA, VPA, KEDA, Cluster Autoscaler) tailored to the highly variable and GPU‑intensive demands of LLM workloads.
• ML Workflow Orchestration – Participate in the engineering and optimization of ML pipeline infrastructure, contributing to best practices for pipeline design, resource allocation, and workflow reliability across LLM training and evaluation workloads.
• AI Developer Platform – Own and contribute to the architecture and operations of interactive compute environments used by AI researchers and LLM engineers to develop, experiment, and prototype.
• CI/CD & GitOps – Participate in the development and ongoing improvement of GitOps workflows and CI/CD pipelines, contributing to deployment best practices and enabling rapid, reliable delivery of platform changes.
• Observability & Reliability – Contribute to the full observability stack implementation – designing dashboards, defining SLOs, building alerting frameworks, and ensuring deep visibility into LLM workload performance and platform health.
• Cloud Infrastructure – Participate in cloud infrastructure design across compute, storage, networking, and IAM, with a strong emphasis on cost optimization and operational excellence.
• Security & Compliance – Engage actively in the vulnerability assessment and remediation program across all platform components, contributing to security standards and ensuring the LLM platform meets organizational and regulatory compliance requirements.
• Collaborative Engineering – Participate in technical design reviews, contribute to roadmap discussions, and serve as a knowledgeable resource and collaborative partner across AIOps and MLOps disciplines.

Required Experience & Skills

• 7+ years of experience in DevOps, Platform Engineering, MLOps, or a closely related infrastructure discipline.
• Deep Kubernetes expertise – production experience operating Kubernetes at scale on any major managed platform (EKS, GKE, AKS) or on‑premises, with advanced knowledge of scheduling, autoscaling, networking, RBAC, and cluster operations.
• Cloud infrastructure proficiency – extensive experience designing and operating production workloads on at least one major cloud provider (AWS, GCP, or Azure), covering compute, storage, networking, and identity and access management.
• MLOps / AI Infrastructure experience – demonstrated experience building and operating infrastructure that supports ML training, model serving, or LLM workloads, including GPU resource management and scheduling at scale.
• CI/CD & GitOps – strong hands‑on experience with GitOps principles and modern CI/CD pipeline design, using any mainstream tooling (ArgoCD, Flux, GitHub Actions, Tekton, or equivalent).
• Observability Engineering – production experience designing and operating observability platforms including metrics, logging, and distributed tracing, using any modern stack (Grafana/LGTM, Prometheus, Datadog, ELK, or equivalent).
• Infrastructure as Code – strong proficiency with Terraform, Helm, or comparable IaC and configuration management tooling.
• Programming & Scripting – solid coding ability in Python and/or Go, with experience writing automation, tooling, and infrastructure integrations.
• Security Mindset – hands‑on experience with vulnerability scanning, remediation workflows, and cloud security best practices including RBAC hardening and secrets management.

Strongly Preferred

• Direct experience with Flyte or comparable ML workflow orchestration platforms (Kubeflow, Airflow, Prefect, Metaflow).
• Experience operating JupyterHub or equivalent multi‑user interactive compute platforms at scale.
• Familiarity with LLM‑specific infrastructure – model serving frameworks (vLLM, Triton, TorchServe), GPU cluster management, large‑scale distributed training setups.
• Hands‑on experience with AWS (EKS, EC2 GPU families, S3, IAM, VPC) as our current primary cloud environment.
• Experience with FinOps practices – cloud cost attribution, rightsizing, and spot/preemptible instance strategies for ML workloads.
• Relevant certifications: CKA / CKS, AWS/GCP/Azure Solutions Architect or DevOps Engineer, or equivalent.

Who You Are

A systems thinker who understands how architectural decisions ripple across reliability, performance, cost, and security.

Operationally minded – you build things to be observable, maintainable, and resilient from day one.

Deeply curious about AI and LLMs – you understand why the infrastructure you build matters and stay current with how the AI landscape is evolving.

Proactive and ownership‑driven – you identify problems before they become incidents and drive solutions to completion.

An effective collaborator and communicator who can translate complex infrastructure concepts for AI researchers, data scientists, and engineering leadership alike.

Comfortable operating with autonomy in a fast‑moving environment where priorities evolve alongside the AI landscape.

Why This Role Stands Out

LLM infrastructure is one of the most technically demanding and strategically important engineering domains in the industry today.

As a senior member of our AIOps team you will directly shape the platform that enables LLM development and productionization – your contributions will have immediate, measurable impact.

You will work on genuinely hard infrastructure problems – GPU scheduling, large‑scale distributed workloads, high‑throughput model serving, and multi‑tenant ML environments.

You will be positioned at the epicenter of the AI infrastructure space, one of the fastest growing and highest‑demand engineering disciplines in the industry.

You will have a clear voice in technical direction – your experience and opinions on platform design are genuinely valued and actively sought.

You will bring your full experience to the table – whether you’ve built on AWS, GCP, Azure, or hybrid environments, your platform engineering expertise is what drives impact here.

What’s In It For You

• Competitive Compensation: Attractive salary, comprehensive benefits, and generous time‑off policies.
• Flexible Work Schedules: Enjoy 4‑day summer work weeks and a winter holiday break.
• Retirement Support: 401(k) / DCPP matching.
• Performance Rewards: Annual bonus program to recognize your contributions.
• Innovative Workspace: Casual, dog‑friendly offices designed for creativity and collaboration.

Vacancy Status

This posting is for an existing vacancy.

Base Salary

CAD $92,500 to $136,000 annually. Final base salary will be determined based on geographical location, experience, and qualifications.

Benefits

Join a company that values your total wellbeing. CARFAX offers competitive compensation, comprehensive healthcare coverage, and the chance to make a meaningful impact in an industry‑leading organization.

US Equal Opportunity Employer Statement

CARFAX is an affirmative action/equal opportunity employer. It is the policy of CARFAX to provide equal employment chance to all persons regardless of race, color, sex, pregnancy, religion, national origin, age, ancestry, citizenship status, veteran status, military status, disability or handicap, sexual orientation, genetic information or any other status protected by federal, state or local law.

In addition, CARFAX will provide reasonable accommodations for qualified individuals with disabilities.

We maintain a drug‑free workplace. We are a participant in E‑Verify.

Canadian Equal Opportunity Employer Statement

CARFAX Canada is an equal opportunity employer, and all qualified candidates will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law.

We’re committed to providing accommodations by request for candidates taking part in all aspects of the recruitment and selection process. For a confidential inquiry or to request an accommodation, please contact your recruiter or email View email address on ca.talent.com.