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Salary: USD 120,000 - 180,000 / annual
F5's Customer Experience Organization is hiring a Senior Forward Deployed Engineer to lead AI and automation initiatives across enterprise systems and internal productivity. This is a hands-on senior individual contributor role focused on greenfield builds rather than sustaining existing systems.
You will split your time across two core mandates:
1. **AI for the SDLC & Internal Productivity**: Prototype and ship internal tools that increase team throughput, including AI-assisted coding workflows, automated code review, test generation, and AI-driven documentation (PRDs, user stories, acceptance criteria). You'll roll out and tune AI developer platforms (Claude Code, Gemini Enterprise, GitHub Copilot) across engineering teams, establishing standards, prompt patterns, and guardrails. You'll instrument everything you build, measuring outcomes like cycle time, review latency, defect escape rate, and hours saved.
2. **Enterprise Applications & Agentic Systems**: Design and deliver AI automation across business systems including GTM and RevOps copilots, support triage and resolution, quote-to-cash automation, and end-to-end process workflows. You'll build production agentic systems using LangChain, LangGraph, MCP, or equivalent technologies, handling multi-agent orchestration, tool calling, memory management, human-in-the-loop checkpoints, and stateful workflow design. You'll architect enterprise RAG systems over business and product data, managing ingestion, chunking, vector stores, hybrid search, reranking, and embedding models. You'll establish prompt engineering as a discipline with versioning, structured outputs, regression testing, and systematic evaluation.
You'll also lead integration design across Salesforce (Flows, Apex, Agentforce/Einstein), Oracle applications, ServiceNow/Zendesk, MuleSoft/Workato, and internal APIs. You'll build reusable connector libraries and self-serve intake paths to reduce bespoke work. You'll own CI/CD for AI workloads, including automated eval gates, model and prompt versioning, and deployment orchestration. You'll keep production AI observable and reliable through monitoring, alerting, and data integrity practices.
As a technical leader, you'll define AI engineering standards for the CX organization, mentor AI and automation engineers, partner with Enterprise Architecture on governance and security, and present architectures and ROI to senior leadership.
**Requirements:**
- 8+ years of software engineering experience, including 3+ years hands-on with AI/ML systems, LLM application engineering, or enterprise intelligent automation
- Expert Python: production-quality code, REST API design, async patterns, and reusable framework design
- Production agentic AI and RAG systems you have shipped and operated (not demos); fluency with retrieval strategy, eval design, and failure modes
- Strong enterprise business systems background with hands-on Salesforce (Flows, Apex, CPQ, and/or Agentforce/Einstein) and familiarity with Oracle application stacks
- Experience with enterprise integration platforms (MuleSoft, Workato, Boomi, or equivalent) across distributed SaaS ecosystems
- Cloud platforms (AWS, GCP, or Azure), containerization (Docker/Kubernetes), CI/CD (GitHub Actions or equivalent), secrets management, and least-privilege access
- Daily hands-on use of AI coding and productivity tooling in your own workflow
- Working knowledge of GTM, RevOps, and CX business processes; ability to turn vague business asks into scoped technical designs
- Bias toward shipping: prototype to learn, measure what you ship, drive problems to resolution without waiting for permission
**Nice to Have:**
- Oracle and/or Salesforce development at enterprise scale; Architect-level certification
- LLM security: prompt injection defense, data leakage prevention, output filtering, PII handling in production pipelines
- ServiceNow or Zendesk AI for intelligent ITSM automation
- AWS serverless and integration services (Lambda, API Gateway, Step Functions, EventBridge, SQS)
- HashiCorp Vault, AWS Secrets Manager, or similar secrets tooling
- Experience in platform engineering, shared services, or federated AI operating models
