Professional Services Technical Operations Engineer - Agentic AI (Remote)

As a global leader in cybersecurity, CrowdStrike 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 CrowdStrike 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. CrowdStrikers 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 CrowdStrikers 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:

This is a Services TechOps Engineer position on the CrowdStrike Professional Services team, designing, building, and operating the agentic AI systems that amplify our consulting and internal operations teams. You'll own production AI tooling end-to-end, from open-ended stakeholder requests through architecture, security review, rollout, and ongoing operation, using AI agents as personal force multipliers to deliver at amplified velocity and scope. If you thrive where cross-functional collaboration cultivates budding ideas into flourishing production systems, this role is for you.

What You'll Do:

• Lead agentic AI systems end-to-end through iterative refinement, owning design, security hardening, phased deployment, and ongoing maintenance.

• Translate stakeholder requirements into clear technical solutions and seamless user experiences, bridging business and engineering across technical and non-technical audiences.

• Architect multi-agent systems with interoperability, security, scalability, and cost as primary design constraints.

• Define benchmarks for agent performance, accuracy, cost, and reliability; surface what matters to decision-makers.

• Deliver within the approved service portfolio, navigating approval gates and existing infrastructure pragmatically rather than defaulting to net-new proposals.

• Advise leadership on AI strategy and roadmap, mentor team members, and stay current with advancements in LLMs and agentic frameworks.

• Own production incident response across a broad service portfolio: hands-on troubleshooting, runbook maintenance, and participation in a 24/7 on-call rotation.

What You'll Need:

• Effective use of AI agents to deliver at amplified velocity and scope, directing AI through open-ended, multi-step work; comfortable applying adversarial-validation patterns (Challenger agents, confidence scoring, mandatory dissent) to prevent LLM groupthink in production systems.

• Proven track record integrating LLM-based solutions into production at scale, with hands-on experience in at least one agentic framework (CrewAI, LangGraph, ADK, or similar) covering agent lifecycle, tool use, memory, and orchestration.

• Production-quality Python with type hints, testing, and CI/CD discipline, increasingly applied through directing and reviewing AI-generated implementations.

• Systems thinking and full-stack mindset; reasons about how changes propagate across services, data pipelines, frontends, and infrastructure, with deliberate use of REST APIs, CI/CD, automated testing, and gated rollouts (feature flags, staged UAT) to balance iteration speed and safety.

• Working knowledge of AWS across a multi-account production environment (SQS/SNS, DynamoDB, S3, IAM); general familiarity, not cloud architect depth.

• Genuine knowledge-sharing orientation; treats documentation, runbooks, and architecture decisions as primary deliverables, building queryable knowledge systems that turn tribal knowledge into shared understanding.

• Pragmatic delivery in constrained environments; designs around approval gates and existing infrastructure rather than defaulting to greenfield ideals.

• Experience integrating AI with Slack as a delivery surface (bot fra