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 seekinga 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, eliminatemanual 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 responsibleAI 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-artAI/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 DevSecOpsengineers on field validation, data quality automation, code review workflows, and platform security requirements with appropriate humangate-keeping

• Foster a culture of continuous improvement - identifybottlenecks in development processes, recommend enhancements, and proactively contribute to engineering communities of practice

• Actively leverageAI platforms such as Gemini Enterprise, Claude Code,NotebookLMetc. 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 recordof 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 proficiencyin 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

• Proficiencyin 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/Agentforcecapabilities

• Familiarity with CI/CD tools (GitHub Actions, Jenkins, or equivalent) and DevOps best practices for deploying AI workloads to production

• Exposure to LLM evaluation frameworks (LangSmith, RAGAS, or similar) and a disciplined, data-driven approach to measuring AI system quality and reliability

• Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure fundamentals sufficient to deploy and manage AI services at scale

Behaviors & Competencies (F5 Fundamental 5)

• Data-Driven, Future-Forward Mindset - stays abreast of emerging AI/ML technologies and applies that knowledge to identifysolutions and capabilities that create a superior user and business experience

• AI/ML Acumen & Responsibility - strong foundationin algorithms, data structures, and programming languages; prioritizes ethics, fairness, and transparency in all AI implementations

• Critical Reasoning - exercises sound judgment, incorporating experience, data, and stakeholder needs to evaluate constraints and arrive at the most beneficial path forward

• Initiative & Adaptability - does more than is required; applies original thinking to improve processes and systems; deals well with ambiguity and moves toward the most logical outcome

• Communication & Collaboration - works seamlessly with diverse teams including data scientists, engineers, designers, and business stakeholders; translates complex AI concepts for varied audiences; unites cross-functional teams around a shared product vision

Qualifications

• Strong grasp of business processes and ability to translate them into scalable, automated technical solutions

• Effective communicator who can bridge technical AI concepts with business stakeholders and non-technical audiences

• Excellent problem-solving capabilities with a strong focus on delivering customer-centric solutions

• Demonstrated ability to operateautonomously, manage multiple workstreams, and thrive in a fast-paced, delivery-oriented environment

• Strong collaboration skills - proactively works with peers on design, best practices, and code reviews

Nice to Have

• Experience with Salesforce, Agentforceand/or Oracle application development at scale in an enterprise context

• Familiarity with HashiCorpVault, AWS Secrets Manager, or similar tools for secure credential and secrets management

• Knowledge of LLM security best practices - prompt injection defense, data privacy controls, output guardrails, and PII handling in AI pipelines

• Exposure to ServiceNow or Zendesk AI automation, ITSM triage, or intelligent case routing workflows

• Experience with AWS serverless and integration services (e.g., Lambda, API Gateway, Step Functions, EventBridge, SQSetc.) and a strong understanding of integration layer best practices

• Certifications in AWS, GCP, Azure, or Salesforce platform (Architect-level preferred)

• Familiarity with low-code/no-code configurations and building solutions using platform extensibility features

What Sets You Apart

• You ship AI solutions that work in production - observable, testable, resilient, and measured

• You obsess over quantifiable business impact - hours saved, cycle time reduced, error rates cut

• You bring engineering rigor to AI: eval frameworks, version control, observability - not just prototypes

• You move fast, learn faster, and raise the bar for everyone around you

• You are a builder at heart who leads from the front - bringing intensity, accountability, and pride in craftsmanship

• You thrive in ambiguous environments and consistently deliver beyond expectations

Why Join F5's CX Organization?

The F5 CX organization is on an ambitious journey to become an AI-native enterprise - and the AI engineering work you will do here directly shapes that future. You will work on problems that matter, in a collaborative environment where innovative ideas are not only welcomed but expected. If you are passionate about engineering excellence, building AI systems that create lasting value, and accelerating business outcomes through intelligent automation, this is the role for you.

The Job Description is intended to be a general representation of the responsibilities and requirements of the job. However, the description may not be all-inclusive, and responsibilities and requirements are subject to change.

Please note that F5 only contacts candidates through F5 email address (ending with @f5.com) or auto email notification from Workday (ending with f5.com or@myworkday.com).

Equal Employment Opportunity

It is the policy of F5 to provide equal employment opportunities to all employees and employment applicants without regard to unlawful considerations of race, religion, color, national origin, sex, sexual orientation, gender identity or expression, age, sensory, physical, or mental disability, marital status, veteran or military status, genetic information, or any other classification protected by applicable local, state, or federal laws. This policy applies to all aspects of employment, including, but not limited to, hiring, job assignment, compensation, promotion, benefits, training, discipline, and termination. F5 offers a variety of reasonable accommodations for candidates. Requesting an accommodation is completely voluntary. F5 will assess the need for accommodations in the application process separately from those that may be needed to perform the job. Request by contacting [email protected].