Senior DevOps Engineer, AI Platform

7 or more years of experience in DevOps, SRE, Platform Engineering, Cloud Infrastructure, or a related role.

Strong hands on experience operating production Kubernetes environments and deep knowledge of networking, scheduling, storage, autoscaling, security, and troubleshooting.

Strong Microsoft Azure experience, including AKS, networking, identity, storage, and monitoring. OCI experience is preferred, or demonstrated ability to work across cloud providers.

Strong cloud networking knowledge across virtual networks, subnets, routing, NAT, load balancers, private networking, DNS, TLS, firewalls, ingress, and egress.

Strong experience with Jenkins, Bitbucket, Docker, Terraform, Helm, Kubernetes, and Infrastructure as Code.

Proven experience supporting production web applications and backend services, including REST APIs, microservices, background workers, and asynchronous architectures.

Hands on experience with databases, caching, and messaging systems such as PostgreSQL, Redis, RabbitMQ, or equivalent technologies.

Experience implementing production observability using OpenTelemetry, Grafana, Prometheus, Sentry, cloud monitoring, or similar tools.

Strong Linux, systems, and production troubleshooting skills.

You will frequently receive a technical design for a new AI workload, application, backend service, or platform capability. From that design, you should be able to independently determine and implement the infrastructure needed to run it in production.

Determine the required cloud resources, Kubernetes configuration, namespaces, scaling model, and supporting services.

Configure ingress and egress, private connectivity, DNS, TLS, service communication, identities, and secrets.

Provision and operate dependencies such as PostgreSQL, Redis, RabbitMQ, storage, and other shared services.

Build the Jenkins and Bitbucket CI/CD flow for build, test, container publishing, deployment, validation, and rollback.

Define observability, health checks, dashboards, alerts, capacity monitoring, and operational runbooks before production launch.

Own infrastructure delivery through UAT and production, partnering with architects and engineers when design tradeoffs require discussion.

Experience supporting AI or machine learning platforms, LLM gateways, agent runtimes, RAG pipelines, or MCP services.

Experience with Cloudflare, Envoy, ArgoCD, GitOps, and OpenTelemetry.

Experience building reusable infrastructure platforms for high scale SaaS or customer facing applications.

Experience operating distributed backend systems using RabbitMQ, Redis, PostgreSQL, and similar technologies.

AI or machine learning infrastructure experience is helpful, but not required. Strong experience with Kubernetes, web and backend infrastructure, networking, queues, databases, CI/CD, observability, and production cloud operations is the foundation for this role.

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