F5 Digital is executing an enterprise-scale Agentic AI transformation to enable secure, observable, and production-grade AI agents across the organization.

We are seeking a Senior AI Software Development Engineer to lead the implementation, rollout, and optimization of high-code agent development and orchestration frameworks, as well as enterprise AI integrations, as part of our Gemini rollout and adoption.

This is a highly technical, hands-on engineering role.

The Senior Engineer will develop scalable agentic systems, apply robust engineering standards for AI workflows, and support engineering teams across F5 in building production-ready agents using Gemini, Vertex AI, and internal AI platform infrastructure.

The ideal candidate has strong backend and distributed systems experience, solid hands-on familiarity with LLM architectures and agent frameworks, and a proven track record of writing secure, high-performance production code.

Key Responsibilities :

1. Agentic Architecture & Orchestration :

- Build and optimize enterprise-grade agent orchestration frameworks supporting tool use, memory, RAG, agentic workflows and automation.

- Develop patterns for multi-agent collaboration, event-driven execution, and workflow chaining across enterprise systems.

- Implement standards for agent lifecycle management, state persistence, and context engineering.

2. Gemini & Vertex AI Integration :

- Integrate Gemini models via Vertex AI, ensuring secure, scalable API consumption and robust model routing.

- Develop and maintain internal SDKs, abstractions, and reusable components to standardize Gemini usage across F5 teams.

- Implement and refine prompt engineering, token efficiency, grounding strategies, and structured output patterns.

3. High-Code Agent Enablement :

- Develop reference implementations and reusable libraries for high-code agents in Java, Python, Go, or TypeScript.

- Implement secure integration patterns for agents interacting with Salesforce, Snowflake, SharePoint, ServiceNow, and internal APIs.

- Build and maintain MCP (Model Context Protocol) servers and secure API integration gateways.

4. Observability, Safety & Governance :

- Build logging, tracing, telemetry, and evaluation pipelines for agent performance and reliability.

- Apply safety guardrails including input/output validation, hallucination mitigation, prompt injection defenses, and policy enforcement.

- Collaborate with Security to ensure secure data handling, RBAC enforcement, and compliance alignment.

5. Developer Enablement & Technical Support :

- Guide and support engineering teams adopting Gemini Code Assist, CLI workflows, and internal AI development platforms.

- Create high-quality technical documentation, internal libraries, and code samples for developer teams.

- Participate in code reviews and provide guidance for AI-enabled applications across F5.

6. Performance & Scalability :

- Optimize inference latency, parallelization, and cost management strategies across agent workflows.

- Implement caching strategies, streaming responses, and batching techniques to improve throughput.

- Conduct performance testing and benchmarking of agents/models across different workloads.

Required Qualifications :

- 6+ years of experience in software engineering, with strong experience in distributed systems and backend architecture.

- Deep hands-on coding expertise in Python and at least one of : Go, Java, or TypeScript.

- Hands-on production experience with LLM-based systems, including prompt engineering, tool calling, RAG, embeddings, and agent frameworks.

- Experience with Vertex AI, Gemini APIs, OpenAI APIs, or similar enterprise AI platforms.

- Strong understanding of API design, microservices, and cloud-native architectures (Docker, Kubernetes).

- Experience building or integrating orchestration frameworks (e.g., LangChain, LlamaIndex, custom orchestration layers).

- Familiarity with vector databases, embedding pipelines, and retrieval strategies.

- Strong understanding of authentication, authorization, and enterprise security patterns.

- Proven ability to build robust, reusable code and clean APIs.

Preferred Qualifications :

- Experience building multi-agent systems or autonomous workflow engines.

- Experience with model evaluation pipelines and AI quality metrics.

- Familiarity with structured output enforcement (JSON schemas, function calling).

- Experience working with enterprise data systems such as Snowflake, Salesforce, ServiceNow, SharePoint.

- Knowledge of cost modeling and inference optimization techniques.

- Experience contributing to internal developer platforms or SDK ecosystems.

What Success Looks Like :

- Deliver and optimize a secure, scalable orchestration layer for enterprise agents.

- Enable engineering teams to build production-grade high-code agents with clean, reusable components.

- Maintain robust observability, evaluation, and safety controls for AI-driven workflows.

- Accelerate AI agent rollout by building integrations, SDKs, and ready-to-use reference implementations.