About the role
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. 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: This role is part of CrowdStrike's Core Tech, Go To Market IT Apps Team — a team of Architects, Engineers, QA, BSAs, and Product Owners delivering highly Reliable, Scalable, and Secure Infrastructure and Automation Services across GTM Applications to Accelerate Business Velocity and Operational Excellence. As part of the GTM AI Pod, every member is expected to embrace Agentic AI technologies, operate with an open-source AI engineering mindset, and actively contribute to building the next generation of intelligent GTM workflows. As a GTM AI Lead Engineer, you will lead hands-on engineering delivery of agentic AI solutions within the GTM technology stack. You will be a technical leader and mentor, driving
the team from prototype to production-grade AI-powered systems across Salesforce, Slack, and intelligent workflow automation.
What You'll Do: Lead engineering delivery for agentic AI capabilities for GTM stakeholders across its technology stack(Salesforce, Slack, 3rd party apps) and In-House Platforms. Design and build LLM-powered workflows, autonomous agents,multi-agent systems and AI-enhanced integrations using Agentcore, Slack, MCPs,Langchain Define scalable enterprise AI architecture patterns including model routing, orchestration,memory management and governance strategies Design and optimize Retrieval Augmented Generation(RAG) systems, semantic search pipelines,vector retrieval strategies and enterprise knowledge grounding frameworks. Build and maintain Apex, Lightning Web Components, Salesforce Platform Events, and Agentforce agent actions. Implement AI evaluation frameworks including prompt quality benchmarking, hallucination reduction and model performance monitoring Mentor senior and mid-level engineers; conduct code reviews and enforce AI engineering standards. Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems. Retire legacy integrations and replace them with modern, agentic, event-driven patterns. Champion security-first AI engineering: input validation, output sanitization, and prompt injection hardening. Architect and implement CI/CD pipelines, deployment automation, and platform observability. Embrace Agentic AI and LLM-powered tooling as a core part of your engineering practice. Stay current with rapidly evolving AI/ML technologies and apply them pragmatically to GTM systems. Champion automation-first thinking — if it can be agentic, make it agentic. Build internal tools and apps to streamline data access and GTM decision-making. Implement data governance and monitoring frameworks to enhance GTM system quality. Evaluate vendors and AI platforms with a strategic build vs. buy mindset. Identify and automate manual processes to increase organizational leverage. What You'll Need: Bachelor's degree in Computer Science, Engineering, or related field. 8+ years of software engineering experience, with 3+ years in a lead or principal role. Strong proficiency in Python and TypeScript/JavaScript for AI and integration development. Hands-on production experience with Agentic AI frameworks, document parsing and structured extraction pipelines , building enterprise AI applications, autonomous agents and LLM powered systems. Experience with modern AI orchestration frameworks such as LangGraph, Semantic Kernel, CrewAI, AutoGen, MCP, Langchain and similar frameworks Salesforce development experience: Apex, LWC, REST/SOAP integrations, Platform Events, and Agentforce. Proficiency with vector databases and retrieval optimization techniques. Experience with cloud infrastructure including AWS bedrock,Vertex AI, workflow orchestration, CI/CD tooling (GitHub Actions, Copado, Jenkins) and DevOps practices. Solid understanding of LLM limitations, token economics, and model selection trade-offs.
