DevOps Architect

Role overview

Design and build production cloud infrastructure on AWS, combining architecture leadership with hands-on implementation. You will set technical direction for scalable, secure, and cost-conscious systems, advise client stakeholders, and help the engineering team use AI tools productively while maintaining quality and security.

Responsibilities - Design AWS architectures for new deployments and migrations, covering resilience, disaster recovery, account structure, networking, performance, and cost. - Build and maintain infrastructure as code, establish shared practices, and improve CI/CD pipelines. - Write automation in Python and Bash, with contributions to Java or Node.js services as needed. - Lead client workshops and architecture reviews, translate business needs into technical choices, explain trade-offs, and challenge unsound requests. - Apply AI coding assistants to infrastructure and documentation work, reviewing their output for security, correctness, and cost. - Implement observability and support incident response and post-incident reviews. - Build security and compliance controls into infrastructure, and mentor engineers.

Requirements - At least eight years in DevOps, SRE, or cloud infrastructure, including two or more years as an architect or technical lead. - Deep hands-on AWS experience across networking, identity and access, compute, databases, storage, monitoring, and cost management. - Proven systems design experience for scalable, resilient, fault-tolerant distributed systems, with the ability to explain design trade-offs. - Strong Python and Bash skills and working proficiency in Java or Node.js. - Regular practical experience with AI coding assistants, including evaluating their limitations and verifying their output. - Experience leading stakeholder or client discussions, plus hands-on infrastructure-as-code and container or Kubernetes work.

Nice to have - Professional AWS architecture or DevOps certification, or a Kubernetes certification such as CKA or CKAD. - Consulting or professional services experience, experience adding AI or LLM capabilities to internal tools or pipelines, or exposure to Azure or Google Cloud.