Junior DevOps Engineer

About the role

We run production AI infrastructure that serves real clients, real-time, at scale. Voice calls that can't buffer. Context retrieval that can't lag. If a deployment breaks at 2 AM, someone's demo fails at 9 AM. That someone is a paying client. You'll be the person who makes sure that doesn't happen.

Who you are

You're the kind of engineer who treats infrastructure like software. You don't just deploy services. You build systems that stay up, recover gracefully, and are easy for the rest of the team to operate. You've debugged production incidents, optimized cloud costs, hardened Linux servers, and know that reliability comes from good engineering, not luck. You care about security, automation, and making deployments so predictable that they're almost boring.

What you will do

You'll work directly with our infrastructure and engineering teams, with exposure to the CTO, on the production platform that powers enterprise AI systems.

• Design, maintain, and improve our AWS infrastructure across compute, networking, and Kubernetes.
• Build and own CI/CD pipelines using AWS CodePipeline and CodeBuild to ship production services safely and consistently.
• Manage production Kubernetes clusters (EKS), including deployments, node groups, autoscaling (HPA and Cluster Autoscaler), and workload reliability.
• Deploy and optimize GPU workloads across EKS and EC2 for AI inference and infrastructure services.
• Design secure networking using VPCs, IAM, ALBs, NLBs, VPNs, and private networking.
• Build and maintain observability pipelines with OpenTelemetry, Grafana Alloy, and Prometheus to ensure production visibility.
• Troubleshoot production incidents across Linux systems, Kubernetes, networking, and cloud infrastructure.
• Continuously improve reliability, security, and cloud efficiency while automating repetitive operational work.
• Collaborate closely with backend and AI engineers to make deployments boring. Boring is good. Boring means nothing broke.

Must-haves

• Strong hands-on experience with AWS infrastructure, including VPC, IAM, EC2, Lambda, and EKS.
• Experience managing Kubernetes clusters, including deployments, node groups, Horizontal Pod Autoscaling (HPA), and node autoscaling.
• Experience deploying and operating Docker-based workloads in production.
• Familiarity with AWS CodePipeline and CodeBuild or comparable CI/CD platforms.
• Experience configuring Application Load Balancers (ALB) and Network Load Balancers (NLB).
• Experience setting up and managing secure VPN connectivity (WireGuard preferred).
• Experience with OpenTelemetry, Grafana Alloy, and Prometheus for monitoring and observability.
• Strong Linux administration skills, including user and permission management, SSH hardening, filesystem management, and performance monitoring.
• Excellent debugging and troubleshooting skills across infrastructure, networking, and distributed systems.
• A security-first and cost-aware engineering mindset.

Nice-to-haves

• Experience running and optimizing GPU workloads in production.
• Experience managing Linux development environments, including machine images, configuration management, and remote development tooling.
• Experience self-hosting developer or infrastructure tools.
• Contributions to open-source projects or a GitHub profile that demonstrates infrastructure or DevOps work.

About Alchemyst AI

Alchemyst AI is building the context layer for the agentic web - a verifiable context engine that gives AI agents persistent memory, business data, and operational context so they stay accurate, reliable, and production-ready.

We believe the world is moving toward Agent Era 2.0, where context matters more than prompts. Prompts can be auto-optimized; what can't be faked is deep, tractable, verifiable context. That's the thesis we've bet our entire company on.

We're a team of ~10 today. This is the ground floor. You'll have more ownership in your first month here than most DevOps engineers get in their first year elsewhere.