Solutions Architect

Solutions Architects at Launch Darkly are the core of our elite Professional Services organization, helping customers decouple deploy from release to enable rapid, reliable, and safe software delivery. As a key member of the Revenue team, you will apply your technical expertise to guide customers through transformative implementations of Launch Darkly, acting as a trusted advisor throughout their journey. As teams ship AI features faster than ever with coding assistants and agentic tooling, this work is mission‑critical—the blast radius from a faulty chatbot or agentic workflow can be exponential.

As a Solutions Architect – AI, you will lead strategic engagements focused on helping customers adopt and operationalize AI‑powered applications and platforms using Launch Darkly. This includes supporting use cases such as feature‑gated AI rollouts, safe experimentation with LLM‑driven systems, AI governance, and progressive delivery of agentic workflows.

In this role, you will work closely with engineering, platform, and AI leaders to help them integrate Launch Darkly into modern AI architectures, ensuring AI systems are released safely, observed continuously, and optimized through experimentation.

Launch Darkly's Services team is recognized as best‑in‑class among our peers. We are rapidly growing, deeply invested in, and central to the company's strategic vision. You'll be part of a high‑impact organization redefining how teams ship software and AI‑powered products at some of the world's most innovative companies across Technology, Finance & Insurance, Pharmaceuticals & Life Sciences, and Entertainment.

As a Solutions Architect, you'll do more than advise — you'll build alongside customer engineering teams, integrating Launch Darkly directly into their systems and shipping working implementations into production. You'll establish best practices for AI delivery, experimentation, and governance, and then prove them out in code.

• Support Professional Services engagements focused on AI‑enabled and traditional software delivery, helping align customer goals with Launch Darkly best practices.
• Partner with customers to implement feature management into their LLM‑powered applications, agentic workflows, and internal AI platforms.
• Help implement rollout patterns for AI progressive exposure, guardrails, kill switches, and prompt/model experimentation in the customer's codebase, adapting existing patterns to fit each customer's context.
• Contribute to reference implementations, integrations, and prototypes alongside customer engineers writing production‑quality code that demonstrates the pattern.
• Work hands‑on in customer environments to instrument AI systems with Launch Darkly at inference‑time and workflow‑level decision points.
• Help customers building and operating chatbots, copilots, and agent‑based systems integrate Launch Darkly into inference‑time and workflow‑level decisions.
• Gather detailed requirements from customer architectures and recommend solutions to a wide range of technical issues in creative, practical ways.
• Serve as a knowledgeable point of contact for engineering and platform teams during engagements, advising on day‑to‑day implementation questions.
• Resolve common technical obstacles that arise during engagements, escalating novel or highly complex issues as needed.
• Build working knowledge of Launch Darkly and emerging AI delivery patterns to stay current as the landscape evolves.
• Share customer feedback with Product and Engineering teams to inform future AI‑related capabilities.

• You are a self‑starter and excited to take on hard problems.
• You are passionate about helping customers and have a strong sense of ownership.
• You'd rather build the thing than write a deck about it — and you can do both.
• You push back when the obvious answer is the wrong one, and you stay on a problem even when it's technically someone else's.
• You can effectively communicate with experts from different backgrounds, and build strong stakeholder relationships.
• You want to understand how systems actually work — the real version, not the marketing version — which usually means getting into the code.