GENERAL DESCRIPTION:
The Senior Full Stack Engineer ships customer value in our SaaS platform through feature work, customer-facing enhancements, and ownership of bugs and operational support in their area of the product. What sets this role apart is how they work: AI agentic workflows are their dominant dev loop, not a tool they reach for occasionally. We are looking for senior engineers who have built workflows that compound their own velocity — custom agents, MCP servers, code-gen and review pipelines tailored to their stack — and who have sharp judgment about when AI output is shippable and when it needs to be thrown away.
You'll work directly with product, design, and customer-facing teams to deliver features that move the business. Our current platform runs on C# / .NET, Vue.js / TypeScript, MongoDB, and Azure — but we hire for adaptability, not stack match. What we care about more than any specific language is your ability to ramp on a new codebase in days using AI as a working tool, to ship high-quality code at a velocity traditional seniors can't match, and to vet what an agent produces before it lands in main.
This is a hands-on builder role. You write production code most days. You will set the technical bar for a small pod (2–3 engineers) and model AI-leveraged engineering for the team. We are explicitly not hiring this role to ship customer-facing AI products — that work happens elsewhere in the org. We are hiring you because your engineering throughput is multiplied by the agentic workflows you've built and the judgment you exercise on AI-generated code.
KEY RESPONSIBILITIES:
- Ship features and customer-facing enhancements across the full stack, working from product specs and customer feedback.
- Use AI-assisted development tooling (Claude Code, Cursor, Codex, etc.) as a first-class part of the dev loop — not as autocomplete, but as the primary pattern by which you produce, review, and validate code.
- Build, share, and refine internal workflows that compound your velocity and the pod's — custom MCP servers, code-gen agents, automated review or test pipelines, codebase-exploration agents.
- Validate AI-generated code before it ships — write tests, run it, review it, and exercise sharp judgment about when output ships, needs rework, or should be thrown away.
- Investigate and fix customer-reported bugs in your area of the product, including in code you didn't write — including code an agent wrote.
- Ramp quickly on unfamiliar codebases using AI as a working tool — design documents, code search, behavior reproduction.
- Contribute to monolith decomposition and modernization as touch points emerge in your work.
- Implement and maintain modern automated testing — unit, integration, and the scaffolding that AI-generated changes warrant.
- Collaborate with cross-functional teams to define, design, and ship; coordinate deployments and incident response with peers.
- Advocate for incremental delivery and contribute to the engineering culture that surrounds you.
QUALIFICATIONS:
Required:
- 6+ years of professional software development experience, including production ownership of customer-facing features.
- Polyglot full-stack engineer. You've shipped production code across multiple languages and stacks, and you ramp on a new one in days, not months. Language is a tool, not an identity. Comfort with at least one statically-typed backend language, at least one modern frontend framework, and both NoSQL and SQL data stores is assumed.
- AI-assisted dev tooling is your dominant dev loop. You use Claude Code, Cursor, Codex, Copilot, or an equivalent daily — and you can describe specifically how your work changes because of it. "I use Copilot for autocomplete" is not what we're hiring.
- You have built workflows that compound your own velocity. Custom MCP servers for your dev environment, code-gen agents tailored to your stack, automated review/test/refactor pipelines, codebase-exploration agents — concrete examples expected. We are looking for engineers who have already moved past using AI on the side.
- Sharp judgment about AI output. You can describe specific instances where you identified hallucinated or wrong AI-generated code and rejected it. You have a personal validation discipline — tests, runs, review — for everything an agent produces before it ships.
- Hands-on experience with at least one mainstream agent framework or SDK (Claude Agent SDK, OpenAI Agents SDK, LangGraph, Pydantic AI, Mastra, or equivalent), even if only for personal or internal dev tooling. We want an opinion about strengths and weaknesses, not just a name-drop.
- Prompt engineering at a senior level. You debug a misbehaving agent — including the agents in your own dev loop — by reading the trace, not by guessing. You've used structured outputs, tool-call schemas, and reasoning models in production, whether for your own workflows or for features you've shipped.
- Eval-first mindset applied to AI-generated code. AI output doesn't ship until you've validated it. You have a personal discipline for this — automated tests, runs, ship/no-ship judgment, regression checks when you change your workflows. Familiarity with eval tooling (Braintrust, LangSmith, Langfuse, Inspect, OpenAI Evals, or equivalent) is welcome; the discipline matters more than the tooling.
- Strong debugging skills — you can trace issues through complex systems, reproduce edge cases, and identify root causes in code you didn't write, including code an agent wrote.
- Fast ramp on unfamiliar codebases with AI as a working tool — you understand how a system works by exploring it, not by waiting for documentation.
- Working understanding of CI/CD pipelines and optimizing DevEx for a dev team.
- Understands SOLID, DRY, KISS, YAGNI, and TDD and applies them when warranted.
- Excellent communication; can work directly with product managers, designers, and customers to triage and scope work.
- Working understanding of security best practices.
Nice to Have:
- Working production knowledge of Model Context Protocol (MCP) — has built or extended MCP servers (for your own dev environment, your team's, or a customer surface).
- Multi-agent orchestration in personal dev tooling — e.g., orchestrating sub-agents for different review, test, or refactor passes.
- Experience deconstructing monoliths into modular or service-oriented architectures.
- Understanding of event-driven architectures and message-based integration.
- Production experience with reasoning models, structured outputs, and tool-call schemas — for your own dev workflows or for shipped features.
- Has shipped customer-facing AI features — welcome context, but not the primary signal we're hiring on for this role.
BENEFITS:
- Comprehensive and competitive health benefits plan
- Matching 401k contributions
- 20 days annual PTO
- Primarily remote work with occasional annual team onsites.
This is a fully remote position open to candidates based in the United States. Compensation: ~$160,000 base salary, commensurate with experience, plus benefits.