Senior AI Engineer – Enterprise Applications

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.

The F5 Digital Organization is at the forefront of modernizing how we operate — delivering innovative, scalable, AI-powered digital solutions that empower business functions across the company. Within the CX Organization, we are accelerating our journey to become an AI-native enterprise, embedding intelligent systems directly into the workflows and platforms that drive revenue, customer success, and operational excellence.

We are seeking a talented, execution-focused Senior AI Engineer who is passionate about designing and deploying AI systems that create real, measurable business impact — not just prototypes. This is a hands-on engineering role at the intersection of large language model (LLM) application development, enterprise SaaS automation, agentic workflow design, and intelligent business process transformation.

In this highly impactful role, you will build and ship production-grade AI agents, RAG pipelines, and platform automations that directly reduce cycle times, eliminate manual effort, and improve productivity across GTM, RevOps, Customer Experience, and Platform Engineering functions. You will partner closely with Product Managers, AI Architects, Business Analysts, and domain stakeholders to translate complex business requirements into elegant, scalable, AI-powered technical solutions. Your decisions will be data-driven, forward-facing, and collaborative — driven by a clear commitment to ROI, engineering excellence, and responsible AI deployment.

Key Responsibilities

AI Engineering & Agentic Systems

• Design, develop, and deploy end-to-end AI agent workflows and intelligent automation pipelines using LangChain, LangGraph, and MCP frameworks, targeting high-priority business processes such as product launch workflows, quoting & pricing automation, lead scoring, case triage, and sales enablement

• Build and operationalize Retrieval-Augmented Generation (RAG) pipelines — including document ingestion, chunking strategies, vector store management, retrieval optimization, and continuous LLM evaluation using LangSmith or equivalent frameworks

• Develop, test, and iterate on prompt engineering frameworks — systematic prompt design, versioning, evaluation harnesses, structured output contracts, and improvement loops tied to measurable business outcomes

• Implement state-of-the-art AI/ML solutions that enhance CX systems, processes, and customer-facing products, conducting experiments, applying appropriate algorithms, and constructing production-grade models aligned to business needs

• Evaluate and adopt emerging AI/ML frameworks and tools to enhance performance and scalability — from data ingestion to model deployment — applying learnings to drive continuous improvement of existing systems

Enterprise Integrations & Platform Development

• Develop Python-based integrations and REST API connectors across enterprise platforms including Salesforce, ServiceNow, Zendesk, MuleSoft/Workato, and collaboration tools (Slack/Teams)

• Build and maintain Salesforce automation workflows (SFDC Flows, Apex) and AI-embedded copilots and assistants within the Salesforce/Agentforce/Einstein ecosystem

• Implement scalable, reusable integration patterns and automation accelerators that increase team velocity and reduce time-to-delivery for new use cases

• Contribute to CI/CD pipeline integration for AI workloads using GitHub Actions, ensuring automated testing gates, model versioning, deployment orchestration, and rollback capabilities

• Ensure high system availability, data integrity, and robust monitoring and alerting capabilities by integrating observability practices and tools (Datadog, Splunk, or equivalent) into production AI pipelines

Impact Measurement & Responsible AI

• Capture baseline metrics, design observability dashboards, and publish impact scorecards tracking hours saved, error reduction, cycle time improvement, a…