• 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.

Role Description The DevOps Engineer will support the design, implementation, and maintenance of scalable and secure infrastructure and DevOps processes at Akkadian Labs. You will work with development, QA, and product teams to enable reliable deployments, automate workflows, and improve system observability across Rocky OS-based, AWS-hosted, and on-premises solutions. This is a hands-on technical role focused on execution, continuous improvement, and operational excellence within the DevOps function led by the DevOps Manager. Key Responsibilities - Infrastructure and Environment Management - Support deployment and maintenance of scalable infrastructure in AWS and hybrid cloud environments. - Assist in managing infrastructure-as-code (IaC) using Terraform, CloudFormation, or similar tools. - Help maintain Linux-based environments. - Contribute to containerization efforts using Docker and orchestration via Kubernetes. - AI and Agent Infrastructure Implementation & Support - Work on the design, deployment and management of AI agent workloads, including provisioning compute instances and managing resource scaling for inference-heavy tasks. - Play a key role in building and maintaining model deployment pipelines, including versioning, testing, and rollback of AI models in production environments. - Monitor AI API consumption and infrastructure costs, implementing alerting and controls to prevent runaway usage and support budget visibility. - Coordinate the implementation of infrastructure-level security guardrails for AI systems, including access controls and data isolation for model inputs and outputs. - Observability and Reliability - Manage monitoring and observability efforts using tools such as Prometheus, Grafana, and the ELK stack. - Troubleshoot system issues and contribute to incident response and root cause analysis. - Develop and execute strategies for improving system reliability, performance, and uptime. - CI/CD and Automation - Build, maintain, and optimize CI/CD pipelines using tools such as Jenkins, BitBucket CI/CD, or similar. - Automate routine operational tasks including builds, testing, deployments, and system updates. - Work with engineering teams to integrate pipelines with Akkadian tools. - Security and Compliance - Follow secure DevOps practices and assist in implementing security controls. - Support compliance initiatives and vulnerability remediation efforts. - Collaboration and Documentation - Work closely with DevOps, engineering, QA, and product teams to support deployments and releases. - Maintain documentation for infrastructure, processes, and operational procedures. - Participate in team ceremonies and continuous improvement initiatives. Qualifications - Experience: 5+ years of experience in DevOps, Site Reliability Engineering (SRE), or a related role. - Cloud Expertise: Hands-on experience with AWS (e.g., EC2, ECS, S3, IAM, Lambda, CloudWatch). - Linux Knowledge: Working knowledge of Linux environments. - Containerization: Familiarity with Docker and Kubernetes. - Scripting: Basic to intermediate scripting ability in Python, Bash, or similar languages. - CI/CD: Experience building or maintaining CI/CD pipelines and related tools. - Observability: Exposure to monitoring and observability tools such as Prometheus, Grafana, and ELK. - Security: Understanding of secure DevOps practices and basic compliance concepts. Preferred Qualifications - Experience supporting AI or machine learning workloads, compute environments. - Exposure to AI model deployment pipelines and model versioning practices. - Experience with infrastructure-as-code tools such as Terraform or CloudFormation. - Familiarity with hybrid cloud or on-premises environments. - Exposure to security best practices in DevOps contexts, including AI-specific concerns such as data isolation and access controls. - Experience supporting production systems and participating in on-call rotations. Benefits - Fully remote environment. - Competitive benefits package including medical, dental, vision. - Company-paid life insurance and disability policies. - 401(k) with a generous matching program. - Paid time off.