Engineer III, AI SDLC Engineer; Remote

As a global leader in cybersecurity, Crowd Strike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily.

Our customers span all industries, and they count on Crowd Strike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. Crowd Strikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation.

We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented Crowd Strikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters?

The future of cybersecurity starts with you.

About the Role:

Crowd Strike's engineering organization is scaling its use of agentic AI across the software development lifecycle — from code generation to review to testing and release. As an AI SDLC Engineer, you'll help drive that transformation: building the platforms and workflows that let engineers ship faster, offload well-scoped work to AI agents with confidence, and spend more of their time on the hardest problems.

You'll partner closely with engineering teams to identify friction points across the dev lifecycle and turn them into automated, agent-driven capabilities — with an eye toward efficient use of compute and model spend along the way.

• Design and build agentic workflows that let engineers automate more of the SDLC — from code generation and review to testing and release — with confidence in the output.
• Improve context-grounding systems so agents resolve the right information on the first try, cutting slow, error-prone multi-turn search out of the workflow.
• Tune model-routing so each task runs on the model best suited to it — matching capability to the job at hand, with cost efficiency as one input among several.
• Build observability and quality tooling — dashboards, usage insights, benchmark suites — that give engineers confidence in what agents are doing and how well they're doing it.
• Establish internal benchmarks graded against real-world tasks (bug detection, review quality, latency) to validate improvements empirically rather than by intuition.
• Partner closely with engineering teams to identify high-friction points in their workflows and turn them into automated, agent-driven capabilities.
• Contribute to the broader engineering culture of responsible, effective AI adoption across the organization.

• 5+ years of backend/platform engineering experience, ideally with production Kubernetes systems.
• Hands‑on experience building or operating LLM‑agent infrastructure — harnesses, tool‑integration protocols, subagent orchestration, or similar.
• Strong understanding of LLM application mechanics: context windows, prompt design, caching, and reasoning/latency tradeoffs.
• Proven experience utilizing AI technologies to enhance decision‑making, streamline workflows and processes, improve efficiency and drive business outcomes.
• Comfort working from ambiguous, evolving specs.
• Go and/or Python proficiency.
• Experience building internal developer platforms, observability tooling, or cost/governance…