Senior Software Engineer - ML Ops

CARFAX • London, ON

3d • Hybrid

About The Position

Join Team CARFAX as a Senior Software Engineer - ML Ops. We are looking for a seasoned Senior Software Engineer - ML Ops 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 you've built that experience on is secondary. Our current platform runs on AWS with EKS, Flyte, ArgoCD, JupyterHub, and the LGTM observability stack, and you'll be working within that environment — but we are far more interested in the depth of your platform thinking than in a specific vendor background. If you are 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 — this role was built for you. At CARFAX, we believe in the power of teamwork and value in-person interactions so that we can collaborate and thrive together. This position will require 2 days in the London, ON office per week, subject to change with future business needs. One last thing: Our four-day week continues in Summer 2026!

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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, contributing architectural decisions that balance performance, cost, and reliability across the full platform lifecycle.
• 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. Our current environment is EKS, though equivalent production Kubernetes experience on GKE, AKS, or on-prem is equally valued.
• 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. We currently use Flyte — experience with comparable platforms such as Kubeflow, Airflow, or Prefect translates well.
• 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. We run JupyterHub today, though experience with equivalent multi-user ML development platforms is directly applicable.
• 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. Our current implementation uses ArgoCD — strong experience with GitOps principles and comparable tooling is what matters.
• 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. We use the LGTM stack (Loki, Grafana, Tempo, Mimir) — experience with Prometheus, OpenTelemetry, ELK, Datadog, or equivalent platforms is welcomed.
• Cloud Infrastructure — Participate in cloud infrastructure design across compute (including GPU instance families), storage, networking, and IAM, with a strong emphasis on cost optimization and operational excellence. Our primary cloud is AWS — candidates with strong GCP or Azure backgrounds who are prepared to work in AWS are encouraged to apply.
• 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

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed