AI Engineer

F5 Networks•Seattle, WA

14d•Hybrid

About The Position

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 & 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.

Requirements

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.

Nice To Haves

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.

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.