AI-Directed Software Engineer

Job Summary:

The AI-Directed Software Engineer designs and delivers software across the full Envisionware stack (backend, frontend, native clients, and the AWS 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 spec-driven 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 Dev Ops component, built on AWS. 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 on AWS that ships them. You'll also build AI capabilities into the platform itself, using Amazon Bedrock to deliver agent-driven workflows on AWS.

Operating in an AI-first environment, you'll push the organization from AI-assisted toward AI-delegated software delivery.

AWS experience is required. The rest you can ramp on, but you'll work across all of it:

• Backend:
  Java, Maven, Jersey (JAX-RS), Jackson, Log4j, Tomcat
• Frontend (Angular):
  Angular + Type Script, PrimeNG, Prime Flex, Transloco, Day Pilot
• Frontend (React):
  React, Vite, Type Script, Tailwind CSS
• Data:
  PostgreSQL (runtime + analytics instances), direct SQL
• Messaging:
  Apache ActiveMQ
• Native client: C# / .NET Framework (Windows Zero Client), WiX / MSI installers, Android/iOS
• Cloud (AWS, required):
  Cloud Formation, IAM, VPC networking, S3, EFS, Cloud Watch, multi-tenant cloud architecture
• Containers & Dev Ops:
  Docker, Kubernetes, Helm/Kustomize
• AI Platform:
  Amazon Bedrock (foundation models, Agents / Agent Core, Knowledge Bases, Guardrails), AWS Lambda for agent tools and action groups, Claude Code
• Analytics & tooling:
  Python ETL pipelines, Swagger / OpenAPI

Multi-module monorepo:
Web Services (backend)

• Web Apps (Angular)
• React Web Apps  (Vite/React)
• Native Clients (C#)
• Packages (Docker/K8s).

• End-to-end delivery of features from concept - demo - production, across backend, frontend, and deployment
• Directing AI tools to generate code for spec-driven SDLC, 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 on AWS of the services you build
• Designing and shipping Amazon Bedrock-based AI agents: action groups and tool integrations, Knowledge Base (RAG) retrieval, and Guardrails
• Operating what you build on AWS, including IAM least privilege, cost awareness (token and compute spend), and Cloud Watch observability
• Throughput and cycle time across your assigned work streams
• Continuous improvement of AI-driven development patterns, prompts, and tooling

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, Spring Boot, Angular components, React UIs, SQL, Dockerfiles, K8s manifests, and Cloud Formation templates alike. AI is also part of what you'll ship. Bedrock-based agents will put AI to work inside our products, which raises the bar: agents need guardrails, evaluation,
human-in-the-loop checkpoints where decisions matter, and predictable cost. 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, Kubernetes, and AWS, but one engineer directing AI across all of them can.

• Strong software engineering fundamentals (APIs, distributed systems, debugging, data flows)
• Hands-on AWS experience (3+ years) building and running production workloads; able to reason about IAM, VPC networking, and the cost and failure modes of the services you use
• Infrastructure as code on AWS (Cloud Formation preferred; CDK or Terraform acceptable)
• 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…