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.
Life at F5 is never dull. We are constantly identifying industry trends and disruptions, then innovating to get ahead of future customer needs—creating application services that help the world’s leading organizations deliver their critical business apps faster and with the highest levels of flexibility, security, performance, and support.
But our success isn’t driven solely by what we do. We also care deeply about how we do it. At F5, our culture is how we live, every single day. And it’s producing extraordinary results—not only for our customers, but also for our employees. We understand that your life is about more than just work, so we’re committed to a culture that supports your whole life.
Position Summary
We are seeking a highly experienced and strategic AI Architect to lead the design, governance, and integration of enterprise-scale Artificial Intelligence (AI), Generative AI (GenAI), and Intelligent Automation solutions within the Digital Organization of F5.
This role is focused on defining how AI is securely, responsibly, and cost-effectively adopted across the enterprise, not solely model development. The AI Architect will establish architectural standards, define enterprise AI patterns, govern technology decisions, and guide delivery teams in building scalable AI-enabled business capabilities.
The ideal candidate combines deep technical expertise in AI/ML architecture with enterprise architecture leadership, governance, platform strategy, and business alignment.
Primary Responsibilities
AI Strategy, Solution Architecture & Governance (70% of time)
- Define and own the enterprise AI architecture roadmap aligned with business objectives and measurable KPIs such as cost optimization, productivity improvement, operational efficiency, and revenue growth.
- Drive enterprise AI strategy across Generative AI (GenAI), Agentic AI / Multi-Agent Systems, Predictive AI, and Intelligent Automation / Hyper-automation.
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- Establish reusable AI architectural patterns, standards, and decision frameworks.
- Lead enterprise AI platform strategy including cloud, hybrid, and on-premise deployment models.
- Evaluate build-vs-buy and open-source-vs-commercial AI platform decisions.
- Architect scalable, secure, and production-ready AI solutions including LLMs, RAG, AI Agents and Multi-Agent Orchestration, NLP and Conversational AI, Intelligent Document Processing (IDP), and Predictive Analytics.
- Define enterprise integration patterns with ERP, CRM, ITSM, BPM, data platforms, APIs, and legacy systems.
- Establish enterprise AI governance frameworks covering model explainability, bias detection and mitigation, hallucination monitoring, Human-in-the-Loop (HITL) mechanisms, and AI lifecycle governance.
- Ensure compliance with data privacy, security, regulatory, and ethical AI standards including GDPR, CCPA, and applicable industry regulations.
- Partner with Security and Risk teams to establish AI security standards, including prompt injection defense and model access governance.
- Define enterprise MLOps and LLMOps standards including feature stores, model registries, automated retraining pipelines, drift detection, and evaluation/observability frameworks.
- Establish CI/CD/CT (Continuous Integration / Continuous Delivery / Continuous Training) standards for AI systems.
- Design cost-optimized AI architectures and define ROI measurement frameworks for enterprise AI investments.
- Evaluate and approve AI technologies, vendors, LLM platforms, vector databases, orchestration frameworks, and tooling ecosystems.
- Lead enterprise AI architecture review boards and design governance processes.
- Mentor AI engineers, ML engineers, architects, automation developers, and platform teams.
- Translate business problems into scalable AI solution architectures with measurable business outcomes.
- Present architectural trade-offs and strategic recommendations to executive leadership.
- Collaborate closely with Data Engineering, Security, Infrastructure, Product Management, Legal & Compliance,
Knowledge, Skills and Abilities
- Demonstrated expertise in designing enterprise-scale AI/ML architectures and governing AI adoption across business units.
- Deep understanding of Generative AI, LLMs, RAG architectures, Agentic AI workflows, and MLOps/LLMOps practices.
- Ability to partner with different development, business, and executive teams across geographies.
- Excellent communication and interpersonal skills; ability to present complex architectural decisions to executive stakeholders.
- Strong time management and organizational skills.
- Ability to provide exceptional support to both internal and external customers.
- Ability to prioritize and multi-task in a fast-paced, changing environment.
- Excellent analytical, critical thinking, and problem-solving skills.
- Strong knowledge of AI governance, responsible AI principles, cost architecture (AI FinOps), and regulatory compliance (GDPR, CCPA).
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field; PhD is a plus.
- 12+ years of experience in enterprise technology architecture, software engineering, digital transformation, or platform engineering.
- 5+ years specifically focused on AI/ML solution architecture and enterprise AI implementations.
- Proven experience establishing or leading AI and/or Automation Centers of Excellence (CoE).
- Demonstrated success delivering production-grade GenAI or LLM-based solutions at enterprise scale.
- Hands-on experience with multi-agent orchestration frameworks and AI tooling, including LangChain, LlamaIndex, AutoGen, Model Context Protocol (MCP), OpenAI / Azure OpenAI, Anthropic, and open-source LLMs (Llama, Mistral, etc.); with applied expertise in prompt engineering, RAG pipeline design, LLM evaluation (LangSmith), AI workflow automation, and skills development using Gemini Enterprise and Claude Code.
- Experience with Salesforce Flows, Apex & CPQ automation
- Experience with secrets management and platform security using Vault, CI/CD pipeline implementation via GitHub Actions, and data analysis, impact measurement, and ROI tracking.
- Experience with cloud AI platforms: AWS (SageMaker, Bedrock), Azure AI Foundry, and Google Vertex AI.
- Knowledge of vector databases (Pinecone, Milvus, Weaviate, pgvector), data lakes/lakehouses, and real-time streaming platforms (Kafka, Kinesis).
- Experience with MLOps platforms: MLflow, Kubeflow, Weights & Biases, and AI monitoring tools
- Experience with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI).
- Strong understanding of microservices architecture, event-driven systems, API gateways, REST/GraphQL/gRPC integration patterns, and identity/access management (OAuth, JWT, IAM, Azure AD).
Physical Demands and Work Environment
- Duties are performed in a normal office environment while sitting at a desk or computer table.
- Duties require the ability to utilize a computer, communicate over the telephone, and read printed material.
- Duties may require being on call
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