Sr. Software Engineer - Applied 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 also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. 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:

We are building an AI-first applied engineering team focused on transforming how operations and infrastructure teams work at scale. As a Senior Applied AI Engineer, you will take cutting-edge AI methods and turn them into production systems that directly improve reliability, velocity, and efficiency for engineering organizations.

This role requires a blend of deep AI engineering expertise and strong software engineering discipline. You will work with LLMs, orchestration frameworks (LangChain, LangGraph, MCP), retrieval pipelines, and evaluation harnesses to build copilots, automated decision engines, and large-scale migration frameworks. You’ll be responsible for ensuring these systems are trustworthy, measurable, and safe—with clear evaluation gates, grounding, and hallucination detection before rollout.

Success in this role means moving beyond prototypes and delivering AI applications that engineers rely on daily: copilots that triage incidents, systems that recommend adaptive configurations, services that automate multi-repo code migrations, and evaluators that keep models accountable. You will collaborate closely with SRE, Security, and various Infrastructure Foundations teams.

What You’ll Do:

• Build and ship LLM-powered systems that reduce toil, accelerate remediation, and improve decision-making in operations contexts.

• Design and maintain evaluation frameworks: hallucination tests, regression harnesses, benchmarks, and quality gates for safe rollout.

• Develop retrieval-augmented pipelines (RAG) and data strategies for grounding on logs, telemetry, runbooks, and system metadata.

• Engineer AI copilots and natural-language interfaces to interact with operational data and workflows.

• Create frameworks for large-scale automation such as safe code migration and transformation pipelines.

• Apply adaptive AI techniques to optimize system configurations, predict anomalies, and recommend preventive actions.

• Partner across teams — collaborate with AI Platform (inference/serving), SRE/Infra/Data Service/DC (operational context), and Security (safe usage) while focusing your work on application logic, correctness, evaluation, and safety.

• Implement guardrails and safety systems: prompt injection defenses, PII filtering, constrained decoding, and model observability.

• Build developer-facing SDKs and APIs in Python/Go;intuitive UIs in JavaScript/React for human-in-the-loop workflows.

• Leverage modern orchestration frameworks (LangChain, LangGraph, MCP, semantic routers) to coordinate multi-step, tool-augmented workflows.

What You'll Need:

• Proven experience shipping LLM-based systems into production with measurable impact.

• Expertise in evaluation and testing of LLMs (benchmarks, hallucination/regression tests, grounding metrics).

• Strong programming skills in Python and Go.

• Hands-on experience with LLM orchestration frameworks: LangChain, LangGraph, MCP, agent frameworks, or equivalent.

• Deep understanding of RAG pipelines: embeddings, retrieval quality metrics, re-ranking, and grounding precision/recall.

• Ability to translate ambiguous operational problems into AI-first solutions with clear KPIs.

Bonus Points:

• Experience with fine-tuning/adapters (LoRA, QLoRA, continual learning) and safety tuning.

• Exposure