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, and adoption rates for leadership and stakeholders

Apply a data-driven, future-forward mindset — developing predictive models, analytics systems, and AI applications that enable the business to make informed decisions based on trends, patterns, and large-scale data insights

Prioritize AI/ML acumen and responsibility — recognize and mitigate potential biases in AI systems, understand the ethical implications of AI deployment, and ensure solutions are developed with fairness, transparency, and accountability in mind

Partner closely with Enterprise Architecture, legal, and compliance stakeholders to ensure all AI systems adhere to approved governance standards, security frameworks, and data privacy requirements

Collaboration & Continuous Learning

Partner with Product Managers, AI Architects, Data Scientists, and Business Stakeholders to align technical delivery with business use cases and long-term CX organizational goals

Collaborate with DevSecOps engineers on field validation, data quality automation, code review workflows, and platform security requirements with appropriate human gate-keeping

Foster a culture of continuous improvement — identify bottlenecks in development processes, recommend enhancements, and proactively contribute to engineering communities of practice

Actively leverage AI platforms such as Gemini Enterprise, Claude Code, N otebookLM etc. to drive personal and team SDLC productivity, and advocate for effective AI productivity tool adoption across the organization

Communicate AI/ML capabilities, system designs, and results effectively — fostering understanding for both technical and non-technical audiences

What You'll Bring

Experience

5–8+ years of professional software engineering experience, with at least 2–3 years of focused, hands-on experience in AI/ML engineering, LLM application development, or intelligent automation

Demonstrated track record of delivering complex, high-impact AI systems with speed and quality in production environments

Experience supporting mission-critical, customer-facing systems in production environments, including functional design, prototyping, testing, and defining support procedures

AI/ML & Agentic Systems

Hands-on experience building agentic AI systems using MCPs, LangChain , LangGraph , AutoGen , CrewAI , or comparable multi-agent orchestration frameworks — including tool-calling, memory management, and human-in-the-loop design patterns

Demonstrated proficiency in prompt engineering at scale — few-shot design, chain-of-thought, structured output, and systematic evaluation

Strong experience with RAG pipeline development — chunking strategies, embedding model selection, vector databases (Pinecone, Weaviate , pgvector , or equivalent), hybrid search, and retrieval evaluation

Solid understanding of AI/ML project lifecycle and tools; ability to design, implement, and test new functionality with minimal supervision — consistently applying good software design, implementation, and testing principles

Hands-on experience building with Gemini Enterprise, Claude Code, or equivalent AI productivity platforms in a development and productivity context

Engineering & Platform

Proficiency in Python and REST API development, with the ability to write clean, maintainable, production-ready integration and automation code

Working knowledge of Salesforce platform — Flows, Apex, Process Builder, and/or Einstein/ Agentforce capabilities

Familiarity with CI/CD tools (GitHub Actions, Jen