Senior Software Engineer - Dev Ops in London

Join Team CARFAX as a Senior Software Engineer - Dev Ops. We are looking for a seasoned Senior Software Engineer - Dev 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.

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, 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.
• 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.
• Cloud infrastructure proficiency — extensive experience designing and operating production workloads on at least one major cloud provider (AWS, GCP, or Azure).
• MLOps / AI Infrastructure experience — demonstrated experience building and operating infrastructure that supports ML training, model serving, or LLM workloads.
• CI/CD & GitOps — strong hands-on experience with GitOps principles and modern CI/CD pipeline design.
• Observability Engineering — production experience designing and operating observability platforms including metrics, logging, and distributed tracing.
• 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.
• Security Mindset — hands-on experience with vulnerability scanning, remediation workflows, and cloud security best practices.

Strongly Preferred:

• Direct experience with Flyte or comparable ML workflow orchestration platforms.
• Experience operating JupyterHub or equivalent multi-user interactive compute platforms at scale.
• Familiarity with LLM-specific infrastructure.
• Hands-on experience with AWS as our current primary cloud environment.
• Experience with FinOps practices.
• 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.
• Proactive and ownership-driven — you identify problems before they become incidents and drive solutions to completion.
• An effective collaborator and communicator.
• Comfortable operating with autonomy in a fast-moving environment.

Why This Role Stands Out:

• LLM infrastructure is one of the most technically demanding and strategically important engineering domains in the industry today.
• Directly shape the platform that enables LLM development and productionization.
• Work on genuinely hard infrastructure problems.
• Be positioned at the epicenter of the AI infrastructure space.
• Have a clear voice in technical direction.
• Bring your full experience to the table.

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.