Senior Engineer

Senior Engineer

Full-Time | Engineering | Atlanta, GA or Remote (If Remote, up to 50% Travel to Atlanta)

About the Role

Swordpoint is looking for a Senior Engineer to build our AI systems and to bring our existing systems forward. This is a high-level engineering role with real ownership and very little process between you and the work. You will regularly get a problem with no precedent and a customer already expecting it, and you will be expected to scope it yourself and come back with a plan.

How this role is shaped is partly up to you. Depending on your preference, you can join the team as a senior individual contributor who sets the technical bar and owns the hardest problems, or you can also oversee a small group of engineers and grow into a lead role. Either way, we expect you to raise the standard of engineering here through the work you do and the code you review.

The work runs in two directions at once. We are building AI systems that sit between OEMs, retailers, dealers, and insurers, orchestrating the lead, service, and warranty lifecycle end to end. At the same time, some of our software is load-bearing and revenue-generating and needs to be modernized. You will do both, often in the same quarter, with judgment about which matters more this week. We want someone who is genuinely excited about AI: someone who builds with models and agent frameworks as a primary tool, who thinks about evaluation and reliability as engineering problems rather than prompting tricks, and who uses modern AI development tooling to work at a speed that would have been implausible three years ago.

Key Responsibilities

• Build and ship features across the full stack, from interface to service to data model to deployment

• Build our AI and agent systems, including orchestration across third-party platforms, evaluation harnesses that catch quality regressions, observability into what the systems actually did, and graceful handling when an upstream system misbehaves

• Evaluate existing systems and modernize them incrementally: stabilize what is failing, add meaningful test coverage where there is none, and refactor or replace what has to go without a big-bang rewrite

• Build and maintain integrations across CRM, field service, POS, and partner systems, including the parts where other people's data models do not agree with each other

• Set engineering standards through architecture decisions, code review, and written guidelines that other engineers follow

• Optionally, oversee and mentor a small group of engineers, including reviewing their work and helping set their priorities

• Use AI tooling as a core part of how you work, and help raise the rest of the team's fluency with it

• Scope ambiguous problems independently and communicate tradeoffs clearly to both engineers and non-technical stakeholders

Required Qualifications

• Bachelor's degree in Computer Science or a closely related technical field, or equivalent professional experience

• 3 to 5+ years building and shipping production software at a top-tier technical organization such as Meta, Google, Amazon, Apple, Microsoft, Netflix, or Stripe, or at a leading AI-native company. For this role, where you learned the craft matters

• Full-stack capability: you can build the interface, the service behind it, the data model under that, and the deployment pipeline around all of it. We are too small for engineers who only work in one layer

• Genuine enthusiasm for AI with hands-on depth: you have shipped LLM-backed features to production, you have built or maintained an evaluation harness and can say what it caught, and you treat context engineering, tool use, structured output, and retrieval as engineering problems

• Daily use of modern agentic coding tooling, and a clear point of view on where it helps and where it does not

• The ability to evaluate existing code and decide what needs to be modernized and how, with specific examples of improving a codebase you did not write while continuing to deliver

• Strong fundamentals in testing, CI/CD, observability, and incremental migration strategy

• Experience building integrations against third-party APIs and messy external data

• Comfort navigating ambiguity and shifting priorities in a fast-paced, scaling environment

Preferred Qualifications

• Experience leading or mentoring other engineers, formally or informally

• Experience as one of the first engineers somewhere, setting the technical culture that followed

• Data engineering depth: pipelines, extraction from messy documents, and reconciliation across systems that disagree

• Experience collaborating with distributed teammates across time zones

• Familiarity with home services, retail, field service, warranty, logistics, maritime, or insurance industries