Archived listing. The details below describe a past opening.
Required Skills & Experience
- 5+ years building and operating production delivery systems (CI/CD, infrastructure-as-code, deployment automation, or platform/SRE work), with senior-level judgment about what is safe to ship.
- A track record of shipping software to production reliably and repeatably, and owning what happens after deploy.
- Strong infrastructure-as-code and cloud deployment skills, ideally on AWS (for example Terraform or CDK, containers, and managed compute).
- Strong automated testing and quality-gate practices, including using tests and checks to validate code you did not write by hand.
- Experience building security and compliance into delivery: secrets management, least privilege, and policy or guardrail enforcement in pipelines.
- Demonstrated experience using AI coding agents or AI-assisted tools to ship real work, beyond autocomplete or experimentation.
- Experience with various tools outside of AWS such as Vercel, Railway, Supabase, etc.
Nice to Have Skills & Experience
- Direct experience deploying React and React Native applications on AWS.
- Experience with AWS delivery and AI services such as CodePipeline, CodeBuild, ECS/Fargate, Lambda, API Gateway, Step Functions, S3, DynamoDB, RDS, or Bedrock.
- Experience building internal developer platforms, golden paths, or paved-road workflows (for example Backstage or equivalent).
- Experience with progressive delivery (canary, blue/green, feature flags) and supply-chain security (SBOMs, artifact signing, dependency scanning).
- Experience with policy-as-code (for example OPA) and compliance-by-construction in regulated environments.
- Experience building evals, guardrails, monitoring, or quality gates for AI-generated or AI-embedded systems.
- Experience with advanced testing such as contract, property-based, or mutation testing.
- Experience with geospatial, routing, logistics, transportation, or operations-focused software.
- Experience applying AI within governed enterprise environments, including FERPA-relevant or similarly regulated data.
- Relevant AWS, security, SRE, or platform certifications.
Job Description
OB DESCRIPTION
You own the client’s path to production for AI‑generated and agent‑built software. You determine how code produced by an AI Software Supervisor moves into production, how autonomous systems deploy it, and how it behaves once live. This is a hands‑on engineering role with no direct reports, acting as the single owner of the production route.
You operate inside a three‑person delivery pod:
The Product Builder defines what to build. The AI Software Supervisor directs coding agents. You ensure what they produce ships fast, safely, and reliably.
Your mandate: close the gap between AI‑speed code generation and safe, production‑grade delivery.
What You’ll Own Path to production for all agent‑generated code CI/CD, DevOps, and infrastructure workflows enabling autonomous delivery Reliability, safety, and governance of deployed systems Integration with cybersecurity, QA, and compliance teams AI‑enabled delivery workflows, hooks, and automation logic (Python, Lambda, etc.) Platform behavior in production, including monitoring and operational readiness Evaluation and adoption of new delivery tools (Vercel, Railway, Supabase, etc.)
What You’ll Do Build and operate a fully autonomous delivery pipeline for AI‑generated code. Design workflows that ensure code is not “pass‑and‑go” - every step is validated, observable, and safe. Challenge the status quo of the client’s environment and modernize where needed. Collaborate with cybersecurity and QA to ensure safe, compliant, FERPA‑aligned releases. Stand up infrastructure patterns using AWS (S3, Lambda, API Gateway, CloudWatch) and modern delivery platforms. Own DevOps tooling, CI/CD controls, and environment orchestration. Ensure a small pod deploys and operates like a much larger engineering team. Evaluate emerging AI‑era DevOps tools and integrate them into the platform. Support front‑end and product teams by enabling rapid iteration and safe deployment.
What Success Looks Like
Your performance is measured in two numbers:
- Speed to production - how quickly code ships
- Safety in production - how reliably it runs
You ensure the pod’s output reaches production as fast as AI can generate it, without compromising reliability or student‑data protection.