Top Hybrid DevOps & Platform Engineering Jobs in Boston, MA

Design and build IT systems and processes, focusing on automation, security, and employee experience while collaborating with cross-functional teams.

The role involves leading technology strategy, managing cross-functional teams, and driving cloud adoption and enterprise-wide transformations.

Lead a 6–8 person team managing the Kubernetes fleet and core runtime components (CoreDNS, cert-manager, Gatekeeper). Define technical vision and roadmap, guide migration from Terraform to Operator-driven lifecycle management, perform hands-on architectural reviews and PR reviews, resolve operational incidents, and collaborate with engineering leaders and stakeholders.

Lead design, development, deployment, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Optimize large-scale AI performance (cost, latency, throughput), partner cross-functionally, and contribute to technical vision and roadmap.

The Senior Site Reliability Engineer will mentor teams on observability practices, architect systems for growth, automate developer tasks, and debug production issues.

The Site Reliability Engineer manages AWS Kubernetes infrastructure, ensuring operational excellence, security, and scalability, while implementing reliability improvements and best practices.

Design and develop observability solutions, enhance performance and reliability of applications, support CI/CD workflows, and troubleshoot issues in a public cloud environment.

Lead the Developer Infrastructure team, enhance developer productivity, optimize environments, streamline CI/CD pipelines, and manage team operations.

The Cloud Engineer role involves developing configurations in YAML and JSON, building monitoring solutions, maintaining software applications, and writing custom scripts for various environments.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.