AI Developer

RedStream Technology; Dallas, Fort Worth (TX, TX); On-site; USD 120,000 - 180,000; Full time

Job summary

RedStream Technology seeks a Software Engineer II / Senior Software Engineer to join an AI-native engineering group shaping the future of software delivery. You will help define agentic development practices, review Claude Code outputs, and ensure reliable, production-ready software from day one.

You will collaborate with cross-functional teams to implement plan-first workflows, uphold engineering standards, and evolve AI integration with clear guardrails and documentation.

Qualifications

• 4+ years of professional software engineering with production ownership.
• Experience with .NET 8, TypeScript and modern databases.
• Strong code‑review judgment and the ability to recognize subtly wrong AI output.
• Comfort operating as a director of agentic tools rather than sole author — and the discipline to verify, not trust.
• Solid testing discipline and familiarity with CI‑based quality gates.

Responsibilities

• Run the plan-first workflow on every non-trivial change: explore, plan, review, then implement.
• Direct Claude Code effectively and review its output as rigorously as a senior engineer would review PRs.
• Catch and correct AI errors — wrong problem solved, unintended side effects, standards drift — before they reach review.
• Understand, contribute and improve AI-DLC workflow and skills — created inside plan mode and review before any code is written.

Skills

.NET 8 / C# or TypeScript

EF Core / Drizzle

PostgreSQL

Cockroach DB

React.js

Python

REST + OpenAPI

CI-based quality gates

Education

Bachelor’s degree in computer science, engineering, or related field

Equivalent experience

Tools

Claude Code

CLAUDE.md context files

Job description

Level: Software Engineer II / Senior Software Engineer

Position Summary:

Our client is building the AI-native engineering organization of the future — one where small, durable teams direct AI to deliver software faster and more reliably than traditional models allow.

This role sits inside that effort from day one: working with the latest in agentic AI development, helping shape the practices and platforms the next generation of technology delivery will be built on, and contributing directly to a capability that compounds in value with every project completed.

AI Developers are engineers who drive agentic development day to day. They direct Claude Code through the plan-first workflow, author the plans it executes, review and correct its output, and remain fully accountable for what ships.

Their leverage comes from operating as directors of a capable engineering partner rather than as line-by-line authors — while retaining the engineering depth to know when the AI is wrong.

A standard pod carries one to two AI Developers. Success is measured not by lines of code written but by the reliability and quality of working software reaching production.

Responsibilities:

AI-DIRECTED DEVELOPMENT

• Run the plan-first workflow — on every non-trivial change: explore, plan, review, then implement — never jumping straight to code.
• Direct Claude Code effectively — and review its output as rigorously as a senior engineer would review a teammate’s pull request.
• Catch and correct AI errors — wrong problem solved, unintended side effects, standards drift — before they reach review.
• Understand, contribute and Improve AI-DLC workflow and skills — created inside plan mode and review before any code is written.

ENGINEERING STANDARDS & QUALITY

• Keep implementation on-standard — no raw SQL, standard response envelope, kebab‑case URLs, OTEL instrumentation, no writes to the legacy boundary.
• Maintain CLAUDE.md and settings.json — the pod’s AI context file and permission boundaries; update as conventions evolve.
• Practice context-window discipline — one task per session, dump to PLAN.md and /clear near the 60% threshold, commit at least hourly.
• Keep pull requests under ~400 lines — and scope them to one feature or one slice.

CONTINUOUS IMPROVEMENT

• Use the company standards, slash commands /new-feature, /review-pr, /fix-bug, /write-tests, /go, /catchup — consistently and not selectively.
• Contribute Skills Library Updates — at project close so the next team starts from a higher baseline.
• Feed lessons back to the shared skills — ea-standard, api-standard, and domain skills — via concrete LESSONS_LEARNED.md entries.
• Understand, apply and continuously Improve token usage - Improvise and fine tune token usage with AI- workflow and Incorporation of new skills

Required Qualifications

• 4+ years of professional software engineering with production ownership.
• Fluency in the tech stack: .NET 8 / C# or TypeScript, EF Core / Drizzle, PostgreSQL, Cockroach DB, React.js, Python, REST + OpenAPI.
• Strong code‑review judgment and the ability to recognize subtly wrong AI output.
• Comfort operating as a director of agentic tools rather than sole author — and the discipline to verify, not trust.
• Solid testing discipline and familiarity with CI‑based quality gates.
• Bachelor’s degree in computer science, engineering, or a related field, or equivalent experience.

Preferred Qualifications

• Hands‑on experience with Claude Code or a comparable agentic coding environment.
• Experience maintaining AI context and guardrail files (CLAUDE.md‑style) in a shared codebase.
• Domain exposure relevant to the pod (collision repair operations, fleet, insurance/DRP, or analytics).
• Track record of contributing reusable patterns back to a shared library.

Core Competencies

• Director’s mindset — sets direction for AI tooling and evaluates output critically rather than accepting it at face value.
• Engineering rigor — holds a high bar on standards, test coverage, and code review regardless of whether code was human‑ or AI‑authored.
• Disciplined focus — scopes sessions tightly, commits frequently, and resets cleanly rather than pushing through degraded context.
• Learning instinct — captures what works and what does not and systematically feed it back to improve the team’s shared baseline.
• Collaborative accountability — works within the pod model and does not treat AI speed as an excuse to skip processes.
• Strong team player - comfortable working and partnering with cross functional team to continuously learn at high pace, build and continuously improve