Professional Services Technical Operations Engineer - Agentic AI (remote)

CrowdStrike CrowdStrike · Enterprise · TX +1 · Remote

CrowdStrike is seeking a Professional Services Technical Operations Engineer to design, build, and operate agentic AI systems that enhance consulting and internal operations. This role involves end-to-end ownership of production AI tooling, from stakeholder requests to ongoing operation, leveraging AI agents as force multipliers. The engineer will architect multi-agent systems with a focus on interoperability, security, scalability, and cost, define performance benchmarks, and manage incident response.

What you'd actually do

  1. Lead agentic AI systems end-to-end through iterative refinement, owning design, security hardening, phased deployment, and ongoing maintenance.
  2. Translate stakeholder requirements into clear technical solutions and seamless user experiences, bridging business and engineering across technical and non-technical audiences.
  3. Architect multi-agent systems with interoperability, security, scalability, and cost as primary design constraints.
  4. Define benchmarks for agent performance, accuracy, cost, and reliability; surface what matters to decision-makers.
  5. Deliver within the approved service portfolio, navigating approval gates and existing infrastructure pragmatically rather than defaulting to net-new proposals.

Skills

Required

  • 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.

Nice to have

  • Experience integrating AI with Slack as a delivery surface (bot frameworks, event subscriptions, slash commands); React/TypeScript frontend experience is valued though not required.

What the JD emphasized

  • production AI tooling end-to-end
  • AI agents as personal force multipliers
  • multi-agent systems
  • agentic AI systems
  • production incident response

Other signals

  • AI agents
  • production AI tooling
  • LLM-based solutions at scale
  • multi-agent systems