Principal AI Software Development Engineer

F5 F5 · Enterprise · Hyderabad, India +1

F5 is building an enterprise-scale Agentic AI platform for secure, observable, and production-grade AI agents. This Principal AI Software Development Engineer role focuses on architecting and implementing high-code agent development, orchestration frameworks, and enterprise AI integration, particularly with Gemini and Vertex AI. The role involves designing scalable agentic systems, establishing engineering standards, and enabling teams to build production-ready agents.

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. Support engineering teams adopting Gemini Code Assist, CLI workflows, and internal AI development platforms.

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
  • custom orchestration layers
  • 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 AI agents
  • secure integration patterns
  • agent lifecycle management
  • agentic workflows
  • tool use
  • memory
  • RAG
  • multi-agent collaboration
  • event-driven execution
  • workflow chaining
  • state persistence
  • context engineering
  • Gemini models
  • Vertex AI
  • API consumption
  • model routing
  • internal SDKs
  • reusable components
  • prompt engineering
  • token efficiency
  • grounding strategies
  • structured output patterns
  • Java, Python, Go, or TypeScript
  • Salesforce, Snowflake, SharePoint, ServiceNow, and 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, and pro-code agent builders
  • architectural review
  • guidance for AI-enabled applications
  • inference latency
  • parallelization
  • cost management strategies
  • agent workflows
  • caching strategies
  • streaming responses
  • batching techniques
  • throughput
  • reliability
  • evaluate and benchmark agent/model performance
  • different workloads
  • 10+ years of experience in software engineering
  • distributed systems
  • backend architecture
  • Python
  • Go, Java, or TypeScript
  • 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
  • custom orchestration layers
  • vector databases
  • embedding pipelines
  • retrieval strategies
  • authentication
  • authorization
  • enterprise security patterns
  • reusable platforms
  • 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

Other signals

  • enterprise-scale Agentic AI platform
  • high-code agent development
  • orchestration frameworks
  • Gemini rollout
  • production-grade AI agents