AI Engineer(Remote, IND)

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 sits at the intersection of AI application engineering and platform reliability within 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 a hybrid AI Engineer (Dev + DevOps), you will build and own the full lifecycle of agentic AI solutions: from designing LLM-powered workflows and autonomous agents to engineering the CI/CD pipelines, infrastructure-as-code, platform observability, and DevSecOps practices that make those solutions production-grade and enterprise-ready. 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. You will not hand off your code to another team to deploy — you own it end to end.What You’ll Do:Lead engineering delivery for agentic AI capabilities across GTM stakeholders and technology stacks (Salesforce, Slack, third-party apps, and in-house platforms), owning requirements through production deployment and post-release observability.Design and build LLM-powered workflows, autonomous agents, and multi-agent systems using Agentcore, Slack, Model Context Protocols (MCPs), LangChain, and LangGraph — then ship them via automated pipelines you maintain.Define scalable enterprise AI architecture patterns: model routing, orchestration, memory management, context-window governance, and multi-tenant isolation strategies.Design and optimize RAG systems, semantic search pipelines, vector retrieval strategies, and enterprise knowledge-grounding frameworks for GTM data domains.Build and maintain Salesforce Apex, Lightning Web Components, Platform Events, and Agentforce agent actions, integrating them with AI back-ends through secure, event-driven patterns.Build and operate platform observability stacks (tracing, logging, alerting) and AI-specific metrics while managing infrastructure-as-code (Terraform / CDK) across AWS Bedrock and Vertex AI.Implement DevSecOps and evaluation frameworks: supply-chain security, prompt benchmarking, hallucination reduction, and automated regression testing for non-deterministic outputs.Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems, including circuit-breaker patterns at both the application and infrastructure layers.Retire legacy integrations and replace them with modern, agentic, event-driven architectures, eliminating manual toil through automation and self-healing runbooks.Champion engineering excellence: code reviews, runbook documentation, blameless post-mortems, and capacity planning that spans both application logic and underlying compute.Evaluate AI vendors and platforms with a strategic build-vs-buy mindset, factoring in total cost of ownership, compliance posture, and operational burden.Identify, scope, and automate manual GTM processes to increase organizational leverage and reduce time-to-insight for go-to-market teams.What You’ll Need:Bachelor’s degree in Computer Science, Engineering, or a related field.5+ years of software engineering experience, with meaningful exposure to both application development and platform/infrastructure responsibilities.Strong proficiency in Python and TypeScript/JavaScript for AI application development, automation scripting, and infrastructure tooling.Hands-on production experience with agentic AI frameworks, document parsing and structured extraction pipelines, autonomous agents, and LLM-powered systems at enterprise scale.Solid working knowledge of modern AI orchestration frameworks: LangGraph, Semantic Kernel, CrewAI, AutoGen, MCP, and/or LangChain.Demonstrable experience building and maintaining CI/CD pipelines (GitHub Actions, Jenkins, or Copado) and practicing GitOps or trunk-based delivery for both application and infrastructure code.Proficiency with container and orchestration runtimes (Docker, Kubernetes or equivalent) and familiarity with service mesh, secrets management, and configuration management patterns.Salesforce development experience: Apex, LWC, REST/SOAP integrations, Platform Events, and Agentforce agent actions.Proficiency with vector databases (Pinecone, pgvector, Weaviate, or similar) and retrieval optimization techniques for RAG systems.Solid understanding of DevSecOps practices: supply-chain security, SAST/DAST integration, secrets rotation, and least-privilege cloud IAM.Bonus Points:Salesforce Platform Developer II, Application Architect, or DevOps Engineer certification.Experience building Slack apps, Slack Workflow Builder, or Slack-integrated agentic workflows.Hands-on experience with Salesforce Einstein / Agentforce platform development.Demonstrated contributions to open-source AI or DevOps tooling (LangChain, LlamaIndex, Terraform providers, or similar).Exposure to fine-tuning, RLHF, or model evaluation pipelines on domain-specifi