Top Hybrid DevOps & Platform Engineering Jobs

Supports, designs, and delivers cloud and business platform solutions. Responsibilities include troubleshooting incidents, integrating systems, improving performance and reliability, coordinating delivery milestones, maintaining documentation, supporting testing and deployments, following IT governance, and assessing cybersecurity and data protection risks. This is an 11-week onsite undergraduate internship, primarily based in Lake County, Illinois, with possible placement at other U.S. locations.

Designs and delivers enterprise automations, AI-assisted workflows, and governed integrations across business systems. Leads cross-functional integration projects, operationalizes Glean-based AI workflows, establishes engineering and observability standards, implements error handling and alerting, and mentors automation engineers. The role partners with stakeholders to translate business needs into scalable technical architectures and roadmaps while advancing reliable, secure, and reusable automation practices.

Design and maintain infrastructure for AI applications across cloud, on-premises, and air-gapped classified environments. Build hardened Docker images, Helm charts, Kubernetes deployments, and GitHub Actions CI/CD pipelines. Collaborate with customers on infrastructure requirements and onsite deployments, support system architecture and accreditation efforts including FedRAMP, and improve deployment strategies for LLMs. The role also contributes to technical strategy, team culture, and solving complex infrastructure challenges.

Designs and maintains secure, scalable cloud platform capabilities supporting application delivery, observability, CI/CD, Infrastructure-as-Code, deployment automation, and operational workflows. The role uses Azure, Terraform, ARM templates, GitHub, Kubernetes, Docker, and monitoring tools while embedding DevSecOps and SRE practices. Responsibilities include incident response, root cause analysis, mentoring, cross-functional collaboration, platform escalation support, and participation in a 24/7 on-call rotation.

Senior Site Reliability Engineer responsible for improving platform availability, scalability, resilience, and observability. The role manages AWS and EKS infrastructure with Terraform, builds CI/CD pipelines using GitHub Actions, expands GitOps through ArgoCD and Helm, strengthens disaster recovery, supports incident response, and develops monitoring, alerting, dashboards, and SLOs. The engineer partners with product teams, leads infrastructure initiatives, troubleshoots Kubernetes, and supports high-availability distributed systems.

Own Savvy’s AWS and EKS cloud platform, including cluster architecture, networking, IAM, infrastructure as code, CI/CD, observability, costs, and deployment standards. Operate PostgreSQL, Redis, and background job systems in production, improving reliability, incident response, upgrades, failover, and capacity. Build internal platform tooling, support infrastructure for coding agents and LLM workloads, audit inherited systems, and mentor engineers. The role sets long-term technical direction while enabling product engineers to ship safely and efficiently.

Maintains and supports server infrastructure, troubleshoots hardware and software issues, monitors performance, applies security patches, manages hardware assets, and supports development environments. Provides server-related helpdesk training, maintains technical documentation, contributes to business continuity and disaster recovery plans, and responds to datacenter outages. Requires rotating 24/7 support, independent problem-solving, collaboration with IT teams and vendors, and strong communication skills.

As a Senior Data Platform Engineer, you will maintain and enhance Hinge's data platform, ensuring data accuracy and supporting various teams in data-driven initiatives.

The GCP Infrastructure Engineer secures and optimizes Google Cloud Platform environments, supports incident response, and implements automation and compliance measures.

Drive reliability, autoscaling, and cost efficiency for cloud-based data and machine learning platforms. Manage Google Cloud services with Infrastructure as Code, optimize Kubernetes workloads, operationalize machine learning and Retrieval-Augmented Generation systems, and build CI automation, failovers, schema migrations, observability, and incident-response processes using Python and Bash.