Engineer III, AI SDLC Engineer (Remote)
About The Position
CrowdStrike'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.
Requirements
- 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 dashboards.
Nice To Haves
- Experience with modern AI coding agent frameworks or SDKs.
- Familiarity with graph-based context systems (knowledge graphs, entity resolution) feeding LLM applications.
- Background instrumenting or optimizing large-scale event pipelines.
- Prior experience running a benchmark-driven optimization program (A/B testing, Pareto-frontier tracking) for a production system.
Responsibilities
- 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.
Benefits
- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
- health insurance
- 401k
- paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
