AI-Directed Software Engineer

Job Summary

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 DevOps 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.

Job Description

Job Description

Our Stack

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 + TypeScript, PrimeNG, PrimeFlex, Transloco, DayPilot

Frontend (React)

React, Vite, TypeScript, TailwindCSS

Data

PostgreSQL (runtime + analytics instances), direct SQL

Messaging

Apache ActiveMQ

Native client

C# / .NET Framework (Windows ZeroClient), WiX / MSI installers, Android/iOS

Cloud (AWS, required)

CloudFormation, IAM, VPC networking, S3, EFS, CloudWatch, multi-tenant cloud architecture

Containers & DevOps

Docker, Kubernetes, Helm/Kustomize

AI Platform

Amazon Bedrock (foundation models, Agents / AgentCore, Knowledge Bases, Guardrails), AWS Lambda for agent tools and action groups, Claude Code

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 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 CloudWatch observability
• 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, Spring Boot, Angular components, React UIs, SQL, Dockerfiles, K8s manifests, and CloudFormation 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.

What We're Looking For

Required

• 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 (CloudFormation 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 (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

Strongly Preferred

Hands-on Amazon Bedrock experience

invoking models from code, building agents with tool use / action groups, RAG with Knowledge Bases, and applying Guardrails

• Evaluating and validating LLM and agent output in production (test harnesses, eval sets, failure handling), not just demos
• Familiarity with agent patterns more broadly (MCP, multi-step tool orchestration, human-in-the-loop checkpoints)

Nice to have

Experience with Java/Jersey, Angular or React, PostgreSQL, or library, public-sector, or multi-tenant SaaS domains.

What Success Looks Like (First 90 Days)

• Ship multiple features from concept to demo-ready in ≤5 days each, touching backend, frontend, and deployment where required
• Demonstrate effective use of AI to produce production-quality code across the stack
• Deliver at least one Bedrock-backed agent workflow from prototype to internal pilot, with guardrails, an evaluation approach, and cost visibility
• Establish repeatable, documented workflows for AI-directed development
• Measurably improve delivery speed and consistency across your workstreams
• Validate and harden AI-generated output before it reaches customers
• Help move the team from AI-assisted → AI-directed development

Bottom Line

This is a full-stack + DevOps role on AWS, and that's exactly why AI makes it possible. If you can take an idea, direct AI across Java, TypeScript, SQL, and Kubernetes on AWS, build Bedrock-powered agents into the platform, and deliver something that works in days, this is the job.

Worker Type

Regular

Number of Openings Available

1