AI Engineer

Job Summary

AI Engineer - Customer Success Services (F5)

Location: Hybrid (Hyderabad)

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