AI Engineer

Skip to main contentCareers at F5Sign InAI Engineer page is loadedAI EngineerApplyremote typeHybridlocationsSeattleSan Josetime typeFull timeposted onPosted 3 Days Agojob requisition idRP1037922At F5, we strive to bring a better digital world to life. Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world. We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation.  Everything we do centers around people. That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive.AI Engineer — Customer Success & Services (F5) Location: Hybrid (San Jose / Seattle ) Why this role matters As F5 scales its SaaS and subscription offerings, intelligent automation and AI-driven experiences across support and success workflows are mission-critical. The AI Engineer will design, build, and operate the core ML/AI systems that power self-service, agent assist, knowledge automation, routing, summarization, and safety/observability tooling — delivering measurable improvements in CSAT, deflection, MTTR and agent productivity.  Position summary You will lead the technical vision and delivery for AI systems across the Customer Success & Support portfolio (myF5, case management, knowledge, omni-channel). You’ll translate product needs into robust machine learning architectures, own model lifecycle and MLOps, implement safe RAG/LLM systems and observability, and partner closely with Product, Support Ops, Security/Compliance, and external vendor platforms to ship production-grade solutions. You are both a hands-on engineer able to deliver production code and an influencer who mentors engineers and sets engineering standards.  Key responsibilities Define technical architecture and roadmap for AI capabilities in support workflows: retrieval-augmented generation (RAG), LLM-based assistants, intent classification, summarization, knowledge generation/maintenance, and conversational systems. Lead end-to-end model lifecycle: data pipelines, training, evaluation, fine-tuning, validation, deployment and continuous monitoring (MLOps). Build and operate production-quality ML services and APIs (scalable inference, caching, batching, latency SLAs); write performant, well-tested code (primarily Python). Design and implement safety, privacy, and governance controls for generative systems: hallucination mitigation, provenance/explainability, access control, logging/audit, and data protection (including FedRAMP/GovCloud considerations where required). Full-Stack Development: Design, develop, and maintain scalable systems, combining frontend development using React/Next.js with TypeScript and backend development with Java (Spring Boot, Hibernate) and additional backend languages like Node, Python, or Go.  Backend Expertise with Java: Build high-performance, scalable backend systems using modern Java frameworks (Spring Boot, Hibernate). Ensure APIs, microservices, and integrations are robust, efficient, and secure.  Cloud Services: Implement and maintain cloud-native applications on Azure or AWS, leveraging managed services such as computing, networking, databases (e.g., Postgres, DynamoDB, Cosmos DB), and object storage (e.g., S3, Azure Blob).  Proficient in implementing robust testing strategies for Java applications using frameworks such as JUnit, TestNG, Mockito, Selenium, and Cucumber.  Event-Driven Architecture: Design and implement event-driven systems using tools such as Solace, Kafka, or AWS SNS/SQS, ensuring real-time communication and asynchronous workflows.  DevOps & CI/CD: Create and maintain CI/CD pipelines with tools like GitHub Actions, Azure DevOps, or Jenkins, streamlining deployment processes.  Infrastructure as Code (IaC): Utilize IaC tools like Terraform, ARM, or Bicep to manage cloud...