AI Engineer

About this role

At 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 & S ervices   (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 ( pri marily Python).  

• Design and implement safety,   pri vacy, 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 (S pri n g 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 (S pri n g 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 configurations and provision reliable infrastructure.   

• Containerization & Orchestration: Develop and deploy scalable containerized applications using Docker and Kubernetes (e.g., AKS/EKS).  

• Integrate AI components with platform systems (Salesforce Service Cloud / Experience Cloud, myF5 portal, search engines like   Coveo ), and with Azure/AWS cloud services and data platforms.  

• Instrument KPIs and observability for AI features (deflection rate, CSAT impact, SLA compliance, model accuracy, latency, drift)—use metrics to drive iterations.  

• Prototype, experiment, and evaluate new models and approaches;   maintain   a “research → product” mindset to bring practical,   timely   AI to production.  

• Coach and mentor engineers and data scientists; set best practices for reproducible experiments, feature engineering, model tests, and CI/CD for models.  

What success looks like  

• Significant, measurable increase in self-service adoption and case deflection (quantified percent improvement year-over-year).  

• Demonstrable improvements in agent productivity (e.g., faster average handle time, reductions in escalations) attributable to LLM-assisted tooling.  

• Stable, low-latency ML services with clear observability and alerting; demonstrable model governance (audit trails, reduced hallucination incidents).  

• Cross-functional stakeholders (Support, Security, Product, Sales) report high satisfaction and trust in AI capabilities.  

Required qualifications  

• 6+ years building full-stack systems at scale   

• Strong Experience in React.js, Next.js, TypeScript, JavaScript, Node.js, Python, Go, Java   

• Experience with responsive design and UI/UX best practices.   

• Strong object-oriented programming skills .    

• Hands-on experience with AWS (S3, DynamoDB, Aurora, Kinesis) and Azure (Blob Storage,  CosmosDB , AKS). Proficient in  utilizing   compute , networking, and managed database solutions.   

• CI/CD Tools: GitHub Actions, Azure DevOps, Jenkins   

• Infrastructure as Code: Terraform, B

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