Senior DevOps Engineer
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Key skills for this role
About the Role
This role builds and runs the infrastructure our Generative AI products depend on: the pipelines that ship code, the platforms that run services and models, and the controls tha.
Key Skills for This Role
Responsibilities
- Write and maintain infrastructure as code for reproducible environments
- Own CI/CD pipelines for building, testing, scanning, and deploying applications and services
- Run container platform (EKS, ECS, or Fargate) and deployment workflows including GitOps
- Stand up runtime for AI workloads: GPU capacity, model serving, inference autoscaling, gateways, and caching
- Manage API gateways, networking, load balancing, DNS, and certificates
- Own secrets, identity, and least privilege access across environments
- Run databases in production: clustering, replication, failover, backups, and recovery
- Build monitoring including token usage and GPU utilisation with alerting and service objectives
- Lead reliability and security practice: incident response, policy as code, vulnerability scanning, and cost discipline
Requirements
- 8 13 years of experience in DevOps or related field
- Experience with AWS infrastructure
- Expertise in infrastructure as code, CI/CD, and container platforms
- Experience running AI/ML workloads including GPU capacity and model serving
- Knowledge of security practices, incident response, and cost discipline
Full Job Posting
Role Overview
- This role builds and runs the infrastructure our Generative AI products depend on.
- AI workloads bring their own demands: GPUs, model serving, inference autoscaling, and token cost.
- You should be comfortable owning infrastructure as code, CI/CD, observability, and security on AWS.
What You Will Do
- Write and maintain infrastructure as code so environments are reproducible, reviewable, and quick to recover.
- Own CI/CD: the pipelines that build, test, scan, and deploy applications, agents, and model serving services.
- Run the container platform (EKS, ECS, or Fargate) and the deployment workflows on top of it, including GitOps where it fits.
- Stand up the runtime for AI workloads: GPU capacity, model serving such as vLLM, Triton, or TGI, inference autoscaling, and the gateways and caching that sit in front of the models.
- Manage API gateways, networking, load balancing, DNS, and certificates so services are exposed safely and predictably.
- Own secrets, identity, and least privilege access across every environment.
- Run databases in production: clustering, replication, failover, backups, and recovery.
- Build monitoring into everything, including token usage and GPU utilisation, with alerting and clear service objectives.
- Lead reliability and security practice: incident response, policy as code, vulnerability and container scanning, and cost discipline.
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