{"id":2195701,"url":"https://alion.io/job/envisionware-ai-directed-software-engineer","title":"AI-Directed Software Engineer","company":{"id":1994532,"name":"EnvisionWare","domain":"envisionware.com","url":"https://alion.io/company/envisionware","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States","Canada"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":121000,"max_usd":234000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":413},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon S3","optional":false},{"name":"Angular","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"C#","optional":false},{"name":"Claude Code","optional":false},{"name":"CloudFormation","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Function Calling","optional":false},{"name":"Helm","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"IAM","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Kustomize","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Spring Boot","optional":false},{"name":"SQL","optional":false},{"name":"Swagger","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Vite","optional":false},{"name":"Windows","optional":false},{"name":"ActiveMQ","optional":true},{"name":"Apache Tomcat","optional":true},{"name":"Jackson","optional":true},{"name":"JavaScript","optional":true},{"name":"JAX","optional":true},{"name":"Maven","optional":true},{"name":"PrimeNG","optional":true}],"status":"live","first_seen_at":"2026-10-09T19:16:36Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T19:45:53Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary:\nJob 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.\nThis 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.\nYou'll also build AI capabilities into the platform itself, using Amazon Bedrock to deliver agent-driven workflows on AWS.\nOperating in an AI-first environment, you'll push the organization from AI-assisted toward AI-delegated software delivery.\nJob Description:\nJob Description:\nOur Stack\nAWS experience is required. The rest you can ramp on, but you'll work across all of it:\nBackend: Java, Maven, Jersey (JAX-RS), Jackson, Log4j, Tomcat\nFrontend (Angular): Angular + TypeScript, PrimeNG, PrimeFlex, Transloco, DayPilot\nFrontend (React): React, Vite, TypeScript, TailwindCSS\nData: PostgreSQL (runtime + analytics instances), direct SQL\nMessaging: Apache ActiveMQ\nNative client: C# / .NET Framework (Windows ZeroClient), WiX / MSI installers, Android/iOS\nCloud (AWS, required): CloudFormation, IAM, VPC networking, S3, EFS, CloudWatch, multi-tenant cloud architecture\nContainers & DevOps: Docker, Kubernetes, Helm/Kustomize\nAI Platform: Amazon Bedrock (foundation models, Agents / AgentCore, Knowledge Bases, Guardrails), AWS Lambda for agent tools and action groups, Claude Code\nAnalytics & tooling: Python ETL pipelines, Swagger / OpenAPI\nMulti-module monorepo: WebServices (backend) · WebApps (Angular) · ReactWebApps (Vite/React) · NativeClients (C#) · Packages (Docker/K8s).\n\nWhat You'll Own\nEnd-to-end delivery of features from concept → demo → production, across backend, frontend, and deployment\nDirecting AI tools to generate code for spec-driven SDLC, APIs, UI, SQL, infra config, and workflows\nDecomposing product requirements into AI-executable tasks\nValidation, testing, and hardening of AI-generated output\nKubernetes/Docker configuration and deployment on AWS of the services you build\nDesigning and shipping Amazon Bedrock-based AI agents: action groups and tool integrations, Knowledge Base (RAG) retrieval, and Guardrails\nOperating what you build on AWS, including IAM least privilege, cost awareness (token and compute spend), and CloudWatch observability\nThroughput and cycle time across your assigned workstreams\nContinuous improvement of AI-driven development patterns, prompts, and tooling\nHow AI Changes This Role\nAI 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.\nAI 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.\nEvery 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.\nWhat We're Looking For\nRequired\nStrong software engineering fundamentals (APIs, distributed systems, debugging, data flows)\nHands-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\nInfrastructure as code on AWS (CloudFormation preferred; CDK or Terraform acceptable)\nFull-stack breadth: comfortable moving between backend services, UI, and deployment config in the same day\nWorking familiarity with containers and Kubernetes (or willingness to ramp fast); can debug a failing pod, read a manifest, and ship a Helm change\nDemonstrated experience using AI coding tools (Claude Code, Cursor, Copilot, or similar) to ship real work\nSharp eye for reviewing AI output, especially subtle correctness, security, or deployment issues\nComfort in fast, ambiguous, rapidly changing environments\nBias toward shipping working software over perfect design\nSystems thinking: understanding how components interact at scale\nWillingness to challenge both human and AI-generated assumptions\nStrong written communication: prompting is writing\nStrongly Preferred\nHands-on Amazon Bedrock experience: invoking models from code, building agents with tool use / action groups, RAG with Knowledge Bases, and applying Guardrails\nEvaluating and validating LLM and agent output in production (test harnesses, eval sets, failure handling), not just demos\nFamiliarity with agent patterns more broadly (MCP, multi-step tool orchestration, human-in-the-loop checkpoints)\nNice to have: Experience with Java/Jersey, Angular or React, PostgreSQL, or library, public-sector, or multi-tenant SaaS domains.\nWhat Success Looks Like (First 90 Days)\nShip multiple features from concept to demo-ready in ≤5 days each, touching backend, frontend, and deployment where required\nDemonstrate effective use of AI to produce production-quality code across the stack\nDeliver at least one Bedrock-backed agent workflow from prototype to internal pilot, with guardrails, an evaluation approach, and cost visibility\nEstablish repeatable, documented workflows for AI-directed development\nMeasurably improve delivery speed and consistency across your workstreams\nValidate and harden AI-generated output before it reaches customers\nHelp move the team from AI-assisted → AI-directed development\nBottom Line\nThis is a full-stack + DevOps role on AWS, and that's exactly why AI makes it possible. 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