Remote AI SDLC Engineer: Build Agentic Workflows

CrowdStrike Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Market-leading compensation & equity

Wellness programs

Generous vacation & holidays

Parental & adoption leaves

Professional development

Employee networks & volunteering

Vibrant office culture & world-class**

Job summary

CrowdStrike is seeking an AI SDLC Engineer to scale agentic AI across the software development lifecycle. You will design platforms and workflows that let engineers ship faster, offload work to AI agents, and spend more time on the hardest problems.

You will partner with engineering teams to optimize compute and model spend, improve context grounding, and ensure reliable, cost-conscious agent-driven automation while advancing responsible AI adoption across the organization.

Qualifications

• 5+ years of backend/platform engineering experience.
• Hands-on experience building or operating LLM-agent infrastructure.
• Strong understanding of LLM mechanics: context windows, prompts, caching, latency tradeoffs.
• Proficient in Go and/or Python.
• Experience building internal developer platforms and observability tooling.

Responsibilities

• Design and build agentic workflows for SDLC automation from code generation to release.
• Improve context-grounding systems to resolve right information on first try.
• Tune model-routing to match task with cost efficiency considerations.
• Build observability and quality tooling for engineers to audit agent activity and performance.
• Establish benchmarks to validate improvements with real-world tasks.
• Partner with engineering teams to convert frictions into automated agent-driven capabilities.

Skills

Backend engineering

Kubernetes

LLM-agent infrastructure

Go/Python

Developer platforms

Observability tooling

Cost governance

Tools

Kubernetes

LLM frameworks

Job description

CrowdStrike is seeking an AI SDLC Engineer to scale agentic AI across the software development lifecycle. You will design platforms and workflows that let engineers ship faster, offload work to AI agents, and spend more time on the hardest problems.

You will partner with engineering teams to optimize compute and model spend, improve context grounding, and ensure reliable, cost-conscious agent-driven automation while advancing responsible AI adoption across the organization.