AI-Directed Software Engineer
Job Summary:
The AI-Directed Software Engineer designs and delivers software across the full Cloud Nine stack — backend, frontend, native clients, and the infrastructure that runs them — by directing AI systems to do the bulk of the implementation work. You'll move ideas from concept to demo to production at high speed, decomposing problems into AI-executable tasks, steering and refining AI output, and owning the quality, performance, and customer impact of the result.
This is a full-stack role with a DevOps component. You won't be specializing in one layer. You'll own features end-to-end — from the database schema, through the Java services, through the Angular or React UI, and out through the Kubernetes deployment that ships them.
Operating in an AI-first environment, you'll push the organization from AI-assisted toward AI-delegated software delivery.
Job Description:
Our Stack
You don't need to know all of this on day one, but you'll work across it:
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Backend: Java, Maven, Jersey (JAX-RS), Jackson, Log4j, Tomcat
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Frontend (Angular): Angular + TypeScript , PrimeNG, PrimeFlex, Transloco, DayPilot
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Frontend (React): React, Vite, TypeScript, TailwindCSS
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Data: PostgreSQL (runtime + analytics instances), direct SQL
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Messaging: Apache ActiveMQ
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Native client: C# / .NET Framework (Windows ZeroClient), WiX / MSI installers
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Infra & DevOps: Docker, Kubernetes, AWS (CloudFormation, EFS, S3), Helm/Kustomize, multi-tenant cloud architecture
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Analytics & tooling: Python ETL pipelines, Swagger / OpenAPI
Multi-module monorepo: WebServices (backend) · WebApps (Angular) · ReactWebApps (Vite/React) · NativeClients (C#) · Packages (Docker/K8s).
What You'll Own
- End-to-end delivery of features from concept → demo → production, across backend, frontend, and deployment
- Directing AI tools to generate code, APIs, UI, SQL, infra config, and workflows
- Decomposing product requirements into AI-executable tasks
- Validation, testing, and hardening of AI-generated output
- Kubernetes/Docker configuration and deployment of the services you build
- Throughput and cycle time across your assigned workstreams
- Continuous improvement of AI-driven development patterns, prompts, and tooling
How AI Changes This Role
AI is your primary implementation engine. You're not expected to hand-write every line of code across every layer of this stack — you're expected to direct AI to produce it. You'll use AI to generate Java services, Angular components, React UIs, SQL, Dockerfiles, and K8s manifests alike.
Every AI-generated output is a starting point, not a finished product. You own correctness, edge cases, security, and production readiness. The breadth of this stack is exactly why AI-directed development matters here: no single engineer can be a deep expert in Java, Angular, React, C#, PostgreSQL, and Kubernetes — but one engineer directing AI across all of them can.
What We're Looking For
- Strong software engineering fundamentals (APIs, distributed systems, debugging, data flows)
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Full-stack breadth — comfortable moving between backend services, UI, and deployment config in the same day
- Working familiarity with containers and Kubernetes (or willingness to ramp fast); can debug a failing pod, read a manifest, and ship a Helm change
- Demonstrated experience using AI coding tools (Claude Code, Cursor, Copilot, or similar) to ship real work
- Sharp eye for reviewing AI output — especially subtle correctness, security, or deployment issues
- Comfort in fast, ambiguous, rapidly changing environments
- Bias toward shipping working software over perfect design
- Systems thinking — understanding how components interact at scale
- Willingness to challenge both human and AI-generated assumptions
- Strong written communication — prompting is writing
Nice to have: Experience with Java/Jersey, Angular or React, PostgreSQL, AWS, or retail/POS domain.
What Success Looks Like (First 90 Days)
- Ship mul