• Operate and improve our Kubernetes-based infrastructure in close collaboration with software developers. • Own GitOps-based continuous delivery with ArgoCD and declarative, templated configuration using Kustomize, deployed through our CI/CD pipelines. • Manage Kubernetes clusters end-to-end, including provisioning, scaling, access control, and cost and performance optimization. • Run and maintain self-managed Kubernetes on-premises alongside Azure-managed clusters, including the networking and storage infrastructure that supports them. • Automate operational tasks and ensure our infrastructure remains reproducible, scalable, and well-documented. • Apply modern containerization and cloud-native best practices across the platform. • Troubleshoot and resolve operational issues in Kubernetes environments. • Document your work clearly and accurately.

Role Description We run a distributed, camera-based video monitoring and AI alerting platform. The system spans the full spectrum of modern and legacy infrastructure: an AWS-hosted fleet of Java microservices and workers, a GPU-backed computer-vision inference pipeline, a real-time streaming and presence layer, and thousands of on-premise edge "media boxes" deployed in the field that ingest camera feeds, serve HLS video, and stream events back to the cloud. This is a reliability-first role. Your primary job is to keep a large, mixed operational estate healthy at scale: - Meaningful service objectives - Trustworthy alerting - Sound capacity - Tested disaster recovery - Fast, calm incident response If you think in SLOs, error budgets, and blameless postmortems, and you are happiest when a noisy, fragile system becomes quiet and predictable on your watch, this is your role. What you'll keep reliable - Computer-vision / AI models: frame-based inference services running on GPU EC2 (g4dn-class, AWS Deep Learning AMIs), fed by camera frames from S3 and a Kafka (Amazon MSK) event bus, with Redis (ElastiCache) for state. - Python services: the AI/alerts inference tier and supporting tooling. - Legacy Java services: ~140 Java 8 services and libraries (REST APIs, SQS/SNS workers, Lambda functions) running on Jetty 9.4, deployed to Elastic Beanstalk, ECS, and Lambda. - Edge appliances ("media boxes"): Ubuntu 22.04 / Docker Compose appliances managed remotely over AWS IoT Core secure tunnelling, with Cloudflare tunnels for egress. - Data and messaging backbone: PostgreSQL (RDS) across many schemas, TimescaleDB for analytics, Redis, DynamoDB, Amazon MSK (Kafka), SQS/SNS, and Kinesis. What you'll do Reliability and operations (the core of the role) - Own service objectives. Define SLIs and SLOs for the services that matter, manage error budgets, and use them to drive prioritization and change-rate decisions. - Make observability trustworthy. Own alert quality end-to-end; build the dashboards and the custom metrics, exporters, and instrumentation (CloudWatch, OpenTelemetry) needed to see the system clearly. - Lead incident response. Run incidents calmly, drive mean-time-to-recovery down, and produce blameless postmortems with action items that actually get closed. - Plan capacity and performance. Forecast and right-size compute (especially GPU), Kafka/MSK throughput and partitioning, RDS/TimescaleDB load, and Redis. - Own business continuity and disaster recovery. Backups, replication, failover, and recovery for RDS, MSK, Redis, and the edge fleet. - Keep the edge fleet healthy. Remote diagnosis and recovery over AWS IoT, container auto-update over systemd timers. - Engineer away toil. Write real software (Python, Golang, Bash) to automate operational work. - Govern production change safely. Enforce collaborative, reviewed change management. Delivery and platform (in support of reliability) - Keep CI/CD healthy and safe: CircleCI with Bazel/Gradle builds, OIDC-based AWS auth, container builds to ECR, and EB/ECS/Lambda deploys. - Maintain Terraform for the AWS estate (compute, networking, IAM, databases, messaging, monitoring). - Harden security and compliance: IAM least-privilege, Secrets Manager/KMS, TLS and certificate management. Qualifications - AWS certification is mandatory. A current AWS Certified DevOps Engineer – Professional or AWS Certified Solutions Architect – Professional is strongly preferred. - 10+ years in Site Reliability Engineering or production operations at scale. - Demonstrated SLO/error-budget practice. - Strong production observability skills. - Proven incident command. - Capacity planning and performance experience across compute, databases, and a messaging or streaming system (Kafka/MSK ideal). - Disaster recovery ownership. - Software engineering ability for automation. - Expert with Terraform (or equivalent IaC) and strong Linux administration. - Database operations experience with PostgreSQL. - A reliability mindset. Requirements - Operating GPU workloads and serving computer-vision or ML models in production. - Apache MSK / Kafka and streaming-data operations. - AWS IoT Core at scale. - Managing a fleet of edge / on-premise devices. - Operating and modernizing legacy systems. - Chaos engineering / game-day practice, and capacity modeling. - Familiarity with Bazel in a monorepo; Cloudflare, Cognito/Auth0, API Gateway. How we work, and what we expect from this hire: - Observability must be trustworthy. - Change management is collaborative, never unilateral. - Follow-through over activity. - Prefer the right fix to a quick patch.

• You won’t be in a typical “keep the lights on” SRE role. The work is about turning a fast-growing, stateful system into a predictable, well-automated platform. (provisioning, scaling, rebalancing, recovery) That means reducing operational stress, designing safe automation for traffic-heavy workloads, and building the tooling and patterns that let the system scale without scaling human effort. • You'll work on the kind of problems that only show up at large scale (petabytes of data, thousands of cores, constant ingestion) across a multi-region, multi-account AWS platform running many services on Kubernetes. • Operating EKS clusters across several environments with Karpenter autoscaling, Cilium networking, and ArgoCD-driven GitOps deployments • Managing and evolving a multi AWS account organization, provisioning, networking, access control, and cross-account connectivity • Maintaining the Terraform/Terragrunt IaC platform - modules, automated plan-on-PR / apply-on-merge pipelines, and safe patterns for shared infrastructure • Improving operational tooling around deploys, schema changes, backups, restores, and incident response • Reducing operational load by identifying repeat pain points and eliminating them through code and self-healing automation • Optimizing cloud spend as you go • Participating in on-call and incident response, with a strong focus on making incidents rarer over time. • You'll have room to design and automate, not just respond to alerts. You should join this team if you like deep ownership of production systems and enjoy building the platform layer that everything else runs on.

• Managing large fleets of EC2-based VMs, disks, and networking for data-intensive workloads • Improving operational tooling around deploys, schema changes, backups, restores, and incident response • Working closely with ClickHouse engineers to turn database-level needs into infra-level solutions • Reducing operational load by identifying repeat pain points and eliminating them through code and self-healing automation • Participating in on-call and incident response, with a strong focus on making incidents rarer over time • You’ll have room to design and automate, not just respond to alerts.