• Own and evolve our AWS cloud infrastructure using Terraform • Design, implement, and continuously improve CI/CD pipelines using GitHub Actions • Champion infrastructure security: proactively identify and remediate cloud misconfigurations • Own and improve SRE practices: define SLOs, build alerting and observability solutions • Participate in on-call rotation and own production incidents end-to-end • Maintain build and release environments for development teams • Evaluate and adopt emerging DevOps technologies through structured proof-of-concept testing • Keep documentation, runbooks, and architecture diagrams current and actionable • Provide technical leadership, mentorship, and strategic guidance to the engineering team • Interface with executive leadership to communicate platform strategy, risk, and investment tradeoffs

• Own and develop ZayZoon's infrastructure-as-code using CloudFormation, with an emphasis on serverless resources (ECS, Fargate, Lambda) • Instrument and analyze daily metrics across both infrastructure performance and our Ruby on Rails applications, using AWS tooling (Athena, CloudTrail) and third-party observability platforms (Grafana, OTel, CloudWatch) • Build, optimize, and maintain efficient pipelines (GitHub Actions, CodeDeploy, CodePipeline) to accelerate developer velocity, including modern deployment strategies like blue/green deployments and intelligent auto-scaling • Stay ahead of resource dependencies, particularly databases (RDS, Redshift), including upgrades, playbooks, and downtime planning • Work closely with application developers to serve all of their infrastructure needs. Turning repeatable needs like spinning up environments, running jobs etc. into platform services that can be used by devs whenever they need them. • Project costs and implement AWS cost savings programs and reserved instances • Partner with our risk and security teams to maintain SOC-2 and cybersecurity compliance, and actively evaluate and remediate Critical and High CVEs across all services • Collaborate extensively with app developers on shared metrics, database performance, and load testing • Collaborate extensively with data engineers to facilitate data warehouse development, ELT, and ETL • Participate in our agile process: sprint planning, story grooming, and standup • Champion our SDLC and secure coding practices across everything you ship

• Design, build, and operate the infrastructure layer supporting AI agent workflows in production • Ensure reliability, scalability, and observability of agentic systems across internal and external products • Design and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform services • Manage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent execution • Implement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloads • Utilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure components • Build and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflows • Define and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systems • Collaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systems • Manage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI services • Implement access controls and security best practices across AI infrastructure environments • Document architecture, runbooks, and best practices to support knowledge sharing across the team

• Design, build, and operate the infrastructure layer supporting AI agent workflows in production • Ensure reliability, scalability, and observability of agentic systems across internal and external products • Design and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform services • Manage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent execution • Implement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloads • Utilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure components • Build and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflows • Define and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systems • Collaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systems • Manage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI services • Implement access controls and security best practices across AI infrastructure environments • Document architecture, runbooks, and best practices to support knowledge sharing across the team.