Senior Software Engineer

Alined Consulting Group; Chicago (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Alined Consulting Group in Chicago seeks a Senior Software Engineer to lead design for customer-facing web and mobile experiences, a unified design system, and AI-powered capabilities across products. This high-impact role emphasizes system design, tradeoffs, and technical direction rather than coding line-by-line.

You will plan, decompose, and review work with Product and Design, guiding AI-assisted development, building secure, observable systems on AWS/Azure, and owning end-to-end lifecycle

Qualifications

• Deep server-side experience in modern languages (Java, Python, C#, Kotlin, or Go).
• Strong API and data-model design skills for scalable systems.
• Experience operating production systems in cloud environments (AWS or Azure).
• Familiarity with containers, Kubernetes, and CI/CD pipelines.

Responsibilities

• Plan and design system architecture, data models, and API contracts.
• Decompose tasks, write specs, and guide AI-assisted development.
• Own testing strategy and ensure verifiable releases.
• Build security, reliability, and observability into systems from the start.
• Lead reviews and maintain strong production judgment across teams.

Skills

Server-side

Java

Python

C#

Kotlin

Go

APIs & data models

AWS

Azure

Kubernetes

CI/CD

Security

Tools

AWS

Azure

Kubernetes

CI/CD pipelines

Job description

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.