Principle AI Engineer

F5 F5 · Enterprise · Seattle, WA +1

Seeking a Principal Software Development Engineer to architect and build an enterprise-scale Agentic AI platform, focusing on high-code agent development, orchestration frameworks, and integration with Gemini and Vertex AI. The role involves establishing engineering standards, enabling other teams to build production-ready agents, and ensuring security, observability, and governance.

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
  • vector databases
  • embedding pipelines
  • retrieval strategies
  • authentication
  • authorization
  • enterprise security patterns

Nice to have

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

What the JD emphasized

  • enterprise-scale Agentic AI platform
  • high-code agent development
  • orchestration frameworks
  • enterprise AI integration
  • secure, observable, and production-grade AI agents
  • deeply technical individual contributor role
  • hands-on architect
  • scalable agentic systems
  • engineering standards for AI workflows
  • production-ready agents
  • strong distributed systems expertise
  • deep familiarity with LLM architectures and agent frameworks
  • translate AI theory into secure, production-grade implementations
  • enterprise-grade agent orchestration frameworks
  • tool use
  • memory
  • RAG
  • agentic workflows
  • automation
  • multi-agent collaboration
  • event-driven execution
  • workflow chaining
  • agent lifecycle management
  • state persistence
  • context engineering
  • Gemini models
  • Vertex AI
  • secure, scalable API consumption
  • model routing
  • internal SDKs
  • reusable components
  • standardize Gemini usage
  • prompt engineering
  • token efficiency
  • grounding strategies
  • structured output patterns
  • high-code agents
  • Java
  • Python
  • Go
  • TypeScript
  • secure integration patterns
  • Salesforce
  • Snowflake
  • SharePoint
  • ServiceNow
  • internal APIs
  • MCP (Model Context Protocol) server development
  • secure API mediation
  • logging
  • tracing
  • telemetry
  • evaluation pipelines
  • agent performance
  • reliability
  • guardrails
  • input/output validation
  • hallucination mitigation
  • prompt injection defenses
  • policy enforcement
  • Security
  • secure data handling
  • RBAC enforcement
  • compliance alignment
  • Gemini Code Assist
  • CLI workflows
  • internal AI development platforms
  • technical documentation
  • internal libraries
  • code samples
  • no-code
  • low-code
  • pro-code agent builders
  • architectural review
  • guidance
  • AI-enabled applications
  • inference latency
  • parallelization
  • cost management strategies
  • agent workflows
  • caching strategies
  • streaming responses
  • batching techniques
  • throughput
  • reliability
  • evaluate
  • benchmark
  • agent/model performance
  • different workloads
  • 10+ years of experience in software engineering
  • significant experience in distributed systems and backend architecture
  • Deep hands-on coding expertise in Python
  • Go
  • Java
  • TypeScript
  • Production experience with LLM-based systems
  • prompt engineering
  • tool calling
  • RAG
  • embeddings
  • agent frameworks
  • Vertex AI
  • Gemini APIs
  • OpenAI APIs
  • enterprise AI platforms
  • Strong understanding of API design
  • microservices
  • Kubernetes
  • cloud-native architectures
  • Experience building or integrating orchestration frameworks
  • LangChain
  • LlamaIndex
  • custom orchestration layers
  • Familiarity with vector databases
  • embedding pipelines
  • retrieval strategies
  • Strong understanding of authentication
  • authorization
  • enterprise security patterns
  • Proven ability to build reusable platforms, not point solutions
  • Experience building multi-agent systems
  • autonomous workflow engines
  • Experience with model evaluation pipelines
  • AI quality metrics
  • Familiarity with structured output enforcement
  • JSON schemas
  • function calling
  • enterprise data systems
  • Snowflake
  • Salesforce
  • ServiceNow
  • SharePoint
  • Knowledge of cost modeling
  • inference optimization techniques
  • Experience contributing to internal developer platforms
  • SDK ecosystems
  • Background in AI safety
  • red-teaming
  • model robustness evaluation
  • Deliver a secure, scalable orchestration layer for enterprise agents
  • Enable engineering teams to build production-grade high-code agents with consistent architecture patterns
  • Establish strong observability, evaluation, and safety controls for AI-driven workflows
  • Accelerate AI agents rollout by providing reusable integrations
  • SDKs
  • reference implementations
  • Serve as the technical authority for enterprise agentic AI engineering

Other signals

  • Enterprise-scale Agentic AI platform
  • High-code agent development
  • Orchestration frameworks
  • Gemini rollout