AI Engineer

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/Gov Cloud considerations where required).
• Full-Stack Development:
  Design, develop, and maintain scalable systems, combining frontend development using React/Next.js with Type Script 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.
• Dev Ops & CI/CD:
  Create and maintain CI/CD pipelines with tools like Git Hub Actions, Azure Dev Ops, 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…