Senior Software Engineer

ComPsych Corporation•,

12d•$150,000 - $200,000

About The Position

We're modernizing how ComPsych builds technology — new customer-facing web and mobile experiences, a unified design system, and AI-powered capabilities across our products. That means raising the engineering bar for how we design, build, and operate software, with Engineering, Product, and Design working together end to end. We're looking for a Senior Software Engineer who has hands-on experience with modern, AI-led development practices and wants to help shape how our engineering organization builds software going forward. 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.

Requirements

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.

Nice To Haves

LLM application engineering (RAG, retrieval quality, evals, LLM observability), or modern TypeScript frontend work against a design system.

Responsibilities

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.