DevOps Engineer

Job Summary We are seeking a highly capableSenior DevOps Engineer / Platform Engineerto build, operationalize, and scale the infrastructure and deployment foundation for a strategicsite-builder / network automation platform . This role will focus on creating reliable CI/CD pipelines, production-grade Kubernetes deployment patterns, managed database services, observability, environment reproducibility, secrets management, and Infrastructure as Code across development, testing, staging, and production environments. This engineer will play a critical role in moving the platform from an early-stage, partially manual operating model into a repeatable, supportable, and production-ready DevOps model. The environment includes Kubernetes-hosted services, AWS managed services, workflow orchestration with Temporal, integration with Nautobot, Argo-based promotion flows, and the supporting tooling required for debugging, snapshotting, local development, and production support. This is a hands-on engineering role for someone who can design the right platform patterns, implement them directly, and establish a durable operating model between development and DevOps teams. Key Responsibilities Platform Deployment & CI/CD • Design, implement, and maintain CI/CD pipelines fortesting, staging, and productionenvironments. • Build and maintain deployment workflows that support safe and seamless promotion across environments. • Improve and maintainArgo-based deployment workflowsto enable controlled release progression from test to staging to production. • Establish baseline deployment mechanisms for the site-builder application and related services. • Standardize Kubernetes application packaging and deployment patterns, with a strong preference towardHelm-based lifecycle managementfor complex services and third-party components. • Migrate existing deployments toHelm chartswhere appropriate. Kubernetes & Runtime Platform Engineering • Support the deployment and ongoing operation of services running in Kubernetes. • Improve runtime reliability, resiliency, and troubleshooting for distributed services operating inside shared Kubernetes clusters. • Investigate and harden service-to-service connectivity patterns, especially for workflow components such as workers connecting to the Temporal engine. • Partner with development teams to define production-grade runtime requirements, resource sizing, restart policies, and platform support boundaries. Infrastructure as Code & Cloud Services • Design and implementfully declarative Infrastructure as Codefor managed cloud services, especially in AWS. • Provision and maintain managed data services such asRDS/PostgreSQLandMongoDB-compatible document databasesacross all environments. • Eliminate manual infrastructure setup where possible and replace it with reproducible, version-controlled deployment patterns. • Prepare the platform for future scale across multiple environments and regions through repeatable IaC and GitOps-aligned practices. Data Services, Snapshots & Developer Enablement • Setup and maintainRDS, MongoDB, Redis/cache services , and related dependencies for all environments. • Build tooling and operational processes for: ◦ production and staging database snapshots, ◦ restoring snapshots into development environments, ◦ enabling local debugging and development from realistic data states. • Support creation of local and development environments, includingMinikube-based environment-as-codeapproaches that mirror production behavior as closely as practical. • Improve platform reproducibility so engineers can quickly stand up close-to-production development environments. Workflow Orchestration & Temporal Support • Lead the setup, deployment, and operational support ofTemporalfor workflow orchestration. • Support production operations for Temporal, including troubleshooting performance issues, restarts, scaling concerns, and resource shortages. • Establish maintainable deployment patterns for Temporal using supported packaging and lifecycle management approaches. • Partner with engineering teams to ensure workflow platform reliability and upgradeability over time. Observability, Reliability & Incident Readiness • Design and maintain observability across testing, staging, and production using tools such asPrometheus and Grafana . • Define and implement monitoring for: ◦ service and cluster utilization, ◦ CPU, memory, storage, ◦ IOPS / throughput metrics, ◦ database connections and session counts, ◦ cache hit / miss / coverage metrics, ◦ RDS and MongoDB utilization, ◦ service health and alerting. • Build and maintain logging, tracing, and correlation capabilities, separated appropriately by environment. • Create tools to support deep debugging and operational inspection, including raw database reads, cleanup of unused volumes, and emergency cache invalidation. Security, Access & Secrets Management • Maintain secrets management processes across environments. • Build tooling for short-lived internal token generation and long-lived secret rotation. • Support secure access from deployed services to active production devices and southbound systems. • Help establish credential management patterns for southbound integrations and device-facing access. • Partner with related teams to define safe operational limits and controls for service integrations. External Integrations & Platform Support • Support integration patterns withNautobotand help define safe client-side behaviors such as rate limiting, retry/backoff, and service protection mechanisms. • Partner with application teams to understand and mitigate integration issues such as rate limiting or request rejection. • Support staging and testing by enablingvirtual device environmentswhere needed. • Contribute to end-to-end acceptance testing and production readiness activities. Operating Model & Cross-Functional Execution • Help define an effective operating model between Development and DevOps, whether viaRACI , embedded Agile delivery, or a hybrid support model. • Support deployment readiness, incident management, environment ownership boundaries, and lifecycle responsibilities. • Work closely with software engineering, infrastructure, application owners, and partner teams to drive production readiness and sustainable operations. Required Qualifications • Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. • 7+ years of experience inDevOps, Platform Engineering, SRE, or Infrastructure Engineeringroles. • Strong hands-on experience withKubernetesin production environments. • Strong experience building and maintainingCI/CD pipelinesfor multi-environment software delivery. • Strong experience withArgoCD , GitOps workflows, or equivalent deployment tooling. • Strong experience withHelmand Kubernetes package/deployment lifecycle management. • Experience withAWS managed services , especiallyRDS/PostgreSQL , document databases, and related infrastructure. • Strong experience withInfrastructure as Code , such as Terraform and/or similar declarative tooling. • Experience withPrometheus, Grafana , and modern observability practices. • Experience withRedis/cache services , secrets management, and operational debugging. • Strong Linux, networking, and distributed systems troubleshooting skills. • Strong scripting and automation skills in one or more languages such as Python, Bash, or Go. • Proven ability to work cross-functionally and operate effectively in environments where ownership boundaries are still evolving. Preferred Qualifications • Experience withTemporaldeployment and production operations. • Experience supporting developer platforms with local environment reproducibility using Minikube, kind, or similar tools. • Experience withMongoDB / DocumentDBoperations and restore workflows. • Experience integrating withNautobot , NetBox, or similar infrastructure source-of-truth platforms. • Experience operating in shared-cluster environments with multi-team tenancy and constrained access models. • Experience designing platform patterns for internal products that must scale across regions or multiple deployment footprints. • Familiarity with network automation or infrastructure orchestration platforms is a plus. What Success Looks Like • CI/CD pipelines are reliable, repeatable, and support safe promotion across all environments. • Kubernetes deployments are standardized, maintainable, and production ready. • Managed infrastructure is defined as code rather than through manual setup. • Temporal, databases, cache layers, and observability tooling are stable and supportable. • Development teams can reproduce realistic environments locally for faster debugging and delivery. • Secrets, access patterns, and operational tooling are mature enough to support production-scale operations. • The DevOps operating model is clearly defined and enables faster deployments with less operational risk.