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# Software Engineer

- **Company:** Compsych Management Corporation
- **Location:** United States
- **Salary:** $125,000 - $175,000
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, C Sharp (Programming Language), Computer Programming, Continuous Integration, Software Debugging, DevOps, Python (Programming Language), Software Engineering, SQL Databases, Agentic-AI, Kotlin, Virtual Agents
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer--ff79bbe4-c9b2-4ea5-bccf-318da3c070a1

## About the Role

Software Engineering, Psychiatry and Mental Health, Fortune 500 Customers, Artificial Intelligence (AI), Customer Relations, Product Design, Software Engineering, Computer Programming, Test Automation, Testing, * You write production services in a modern language such as Java, Python, C#, Kotlin, or Go, and can pick up ours quickly.

 * You build APIs and data models other code depends on - REST contracts, request and response validation, authentication, schema design, indexes, migrations, and SQL that holds up on real volume.
 * You ship and operate your own code in the cloud (AWS or Azure) - containers and CI/CD you can debug when the build or the deploy is what's broken, plus on-call experience diagnosing a live issue from logs, metrics, and traces.
 * You write real tests, not coverage - unit and integration, with a view on which belongs where and what a test actually proves.

## Description

You own a well-scoped feature or component end to end - design, build, test, and how it behaves in production.

 * Plan and design within your scope. You translate requirements into a clear plan and specs an AI agent can execute against, working with Product and Design to clarify what''s ambiguous rather than waiting for someone to resolve it.
 * Direct implementation rather than writing it line by line. Break the work into tasks, write the specs, and let AI agents build - and go hands-on in the code when debugging, unfamiliar territory, or a tricky edge case calls for it.
 * Own testing as a design decision. Decide what needs coverage, direct AI to build the tests, and validate that they actually prove what they claim.
 * Review everything with the same rigor, regardless of author. You catch the assumptions AI won''t flag on its own, and you''re accountable for what ships.
 * Own your feature in production. You deploy through your team''s CI/CD pipeline, partner with DevOps rather than treat deployment as someone else''s job, and you''re the one who responds - and rolls back - when something breaks.