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/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 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, Bicep, ARM templates
• Experience automating deployments and streamlining workflows.
• 10+ years software engineering experience (or equivalent), with significant recent experience building and shipping ML/AI systems to production.
• Strong programming skills in Python; experience writing production-quality services and APIs.
• Deep applied ML expertise: model training, evaluation, feature engineering, experimental design, and productionization. Familiarity with deep learning and NLP architectures (transformers/LLMs).
• Hands-on experience with LLMs: fine-tuning, prompt engineering, retrieval-augmented generation, conversational agents, summarization, and mitigation of LLM failure modes.
• Strong MLOps and data engineering experience: building data pipelines, model CI/CD, monitoring, and automated retraining workflows. Familiarity with Spark/Databricks, SQL and large-scale data processing.
• Cloud experience (AWS and/or Azure) and delivering services with production security, identity, and networking concerns.
• Demonstrated ability to translate business requirements (customer success/support workflows) into technical solutions and to communicate complex technical tradeoffs to non-technical stakeholders.
• Bachelor's or advanced degree in Computer Science, Machine Learning, or related field, or equivalent experience.

Skills & behaviors we value

• Strategic technical thinker who pairs vision with pragmatic execution.
• Customer-obsessed: focuses on measurable customer outcomes and operational efficiency.
• Collaborative across product, support, security, and external partners.
• Data-driven: designs experiments and uses metrics to prioritize and iterate.
• Pragmatic AI champion: knows both the opportunity and limits of generative systems and operationalizes them responsibly.