Alined Consulting Group
Senior Software Engineer
Chicago, Illinois, US
Role snapshot
Alined Consulting Group is hiring a Senior Software Engineer on a full-time basis. This is a role based in Chicago, Illinois, US.
- Company
- Alined Consulting Group
- Location
- Chicago, Illinois, US
- Job type
- Full-time
- Posted
- Nov 11, 2026
Who this role suits
The listing does not state a required experience level here. Read the requirements in the description below and apply if they match your background.
How to prepare your application
- Match your résumé to the requirements in the description, using the same wording where it is honest to do so.
- Prepare a short example for each requirement you meet, and be ready to explain any you only partly meet.
- Read what Alined Consulting Group publishes about its product or team, so you can say clearly why this role and this company.
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About the role
We’re hiring a Senior Software Engineer to support a major technology initiative focused on new customer-facing web and mobile experiences, a unified design system, and AI-powered capabilities across products. The initiative is focused on raising the engineering bar for how software is designed, built, and operated, with Engineering, Product, and Design working together end to end.
This is a high-leverage role: you'll spend more time on system design, task decomposition, and technical direction than on writing code line-by-line. You'll use AI tools to generate code, automated tests, and first-pass reviews, then apply your own judgment to validate and refine that output before it ships.
What You'll Do
AI changes what your time goes toward. You're still accountable for the full lifecycle — planning, design, code, tests, review, production — but your hands-on role in each shifts:
- Plan and design as your core contribution. You own problem framing, sequencing, and tradeoffs with Product and Design, and the system design that follows — the data model, the API contracts, the failure modes.
- Direct implementation rather than writing it line by line. Decompose the work, write the specs, and let AI agents build — dropping into the code yourself when debugging or complexity demands it.
- Own testing as a design decision. You decide what needs coverage and at what level, direct AI to build it, and validate that the tests prove what they claim — not just that they pass.
- Build security, reliability, and observability in from the start. They're design inputs, not production afterthoughts — the systems you ship can be operated safely and understood when they fail.
- Review everything with the same rigor, regardless of author, and keep production judgment human. You're accountable for what ships whether you or an agent wrote it. Deployment calls, on-call response, and what an incident teaches you stay yours.
- Raise the bar for how the team works with AI, coaching engineers to specify, delegate, and validate rather than hand-code everything themselves.
What You'll Bring
- Deep server-side experience in a modern language such as Java, Python, C#, Kotlin, or Go. You've owned services end to end, not just features inside someone else's, and can pick up ours quickly.
- APIs and data models other people depend on: REST contracts, versioning, OAuth2/OIDC, schema design, and SQL that holds up under real load.
- Async and event-driven systems in production — Kafka or comparable, idempotency, and a clear view of what at-least-once costs you downstream.
- You've built and operated production systems at real scale on AWS or Azure — containers, Kubernetes or serverless, IaC, and CI/CD pipelines you've built, operated, or materially improved. You've carried the on-call load for them, so you know what “good” looks like independent of who or what wrote the code.
- A testing strategy you can defend — what belongs at unit, integration, and contract level, and how you know a release is verifiable before it ships.
- Reliability and security you've been accountable for: SLOs you’ve been paged against, incidents you led, OWASP and threat modeling applied — and PHI/HIPAA or comparable regulated-data experience.
- Agentic AI is your primary development surface, and you catch what it gets subtly wrong. Not occasional autocomplete, not a hunch — you've shipped and operated real production systems working this way, and can point to what you caught before it shipped.
Bonus: LLM application engineering (RAG, retrieval quality, evals, LLM observability), or modern TypeScript frontend work against a design system.