Principal AI Engineer

F5 F5 · Enterprise · Seattle, WA +1

F5 is building an enterprise-scale Agentic AI platform and seeks a Principal Software Development Engineer to be the technical authority and hands-on architect for high-code agent development, orchestration frameworks, and enterprise AI integration. The role involves designing and implementing scalable agentic systems, establishing engineering standards for AI workflows, and enabling teams to build production-ready agents using Gemini, Vertex AI, and internal AI infrastructure. Requires strong distributed systems expertise, familiarity with LLM architectures and agent frameworks, and the ability to translate AI theory into secure, production-grade implementations.

What you'd actually do

  1. Design and implement enterprise-grade agent orchestration frameworks supporting tool use, memory, RAG, agentic workflows and automation.
  2. Lead technical integration of Gemini models via Vertex AI, ensuring secure, scalable API consumption and proper model routing.
  3. Build reference implementations and reusable frameworks for high-code agents in Java, Python, Go, or TypeScript.
  4. Implement logging, tracing, telemetry, and evaluation pipelines for agent performance and reliability.
  5. Optimize inference latency, parallelization, and cost management strategies across agent workflows.

Skills

Required

  • Python
  • Go
  • Java
  • TypeScript
  • distributed systems
  • backend architecture
  • LLM-based systems
  • prompt engineering
  • tool calling
  • RAG
  • embeddings
  • agent frameworks
  • Vertex AI
  • Gemini APIs
  • OpenAI APIs
  • enterprise AI platforms
  • API design
  • microservices
  • Kubernetes
  • cloud-native architectures
  • orchestration frameworks
  • LangChain
  • LlamaIndex
  • vector databases
  • embedding pipelines
  • retrieval strategies
  • authentication
  • authorization
  • enterprise security patterns
  • reusable platforms

Nice to have

  • multi-agent systems
  • autonomous workflow engines
  • model evaluation pipelines
  • AI quality metrics
  • structured output enforcement
  • JSON schemas
  • function calling
  • Snowflake
  • Salesforce
  • ServiceNow
  • SharePoint
  • cost modeling
  • inference optimization techniques
  • internal developer platforms
  • SDK ecosystems
  • AI safety
  • red-teaming
  • model robustness evaluation

What the JD emphasized

  • enterprise-grade agent orchestration frameworks
  • high-code agent development
  • production-grade implementations
  • production-ready agents
  • enterprise AI integration
  • agentic systems
  • AI workflows
  • agent frameworks
  • orchestration frameworks
  • agent lifecycle management
  • secure integration patterns
  • developer enablement
  • AI-driven workflows

Other signals

  • enterprise-scale Agentic AI platform
  • technical authority and hands-on architect for high-code agent development
  • design and implement scalable agentic systems
  • enable engineering teams to build production-ready agents