Senior Forward Deployed Engineer

F5•San Jose, CA

1d•$120,000 - $180,000

About The Position

The F5 CX Organization builds and runs the enterprise systems behind GTM, Customer Success & Support, RevOps, and Platform Engineering. We are embedding AI into how those systems work, and into how we build them. We are hiring a Senior Forward Deployed Engineer: a hands-on senior individual contributor who moves from business problem to working prototype fast, then hardens what works into production. Your time splits across two mandates: maturing our enterprise applications with AI and automation, and making our own engineering and product teams measurably faster with AI across the SDLC. This is a greenfield build role, not a sustain-the-business role. You will set the technical patterns other engineers follow.

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 the failure modes of both
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, with the ability to turn a vague business ask into a scoped technical design
A bias toward shipping: you prototype to learn, measure what you ship, and drive problems to resolution without waiting for permission

Nice To Haves

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: auto-triage, severity classification, SLA routing
AWS serverless and integration services (Lambda, API Gateway, Step Functions, EventBridge, SQS)
HashiCorp Vault, AWS Secrets Manager, or similar secrets tooling
Experience in a platform engineering, shared services, or federated AI operating model

Responsibilities

Prototype and ship internal tools that raise team throughput: AI-assisted coding workflows, automated code review, test generation and QA automation, and AI drafting of PRDs, user stories, and acceptance criteria
Go from idea to MVP in days. Scope it, build it, demo it, get real users on it, then decide to harden or kill
Roll out and tune AI developer platforms (Claude Code, Gemini Enterprise, GitHub Copilot, or equivalent) across engineering teams, including standards, prompt and context patterns, guardrails, and adoption
Instrument what you build. Cycle time, review latency, defect escape rate, and hours saved, reported as outcomes rather than activity
Design and deliver AI automation across business systems: GTM and RevOps copilots, support triage and resolution, quote-to-cash automation, and end-to-end process workflows
Build production agentic systems with LangChain, LangGraph, MCP, or equivalent: multi-agent orchestration, tool calling, memory management, human-in-the-loop checkpoints, and stateful workflow design
Architect enterprise RAG over business and product data: ingestion and chunking strategy, vector store selection, hybrid search and reranking, embedding model management, and eval loops tied to business KPIs
Establish prompt engineering as an engineering discipline: versioning, structured output contracts, regression test harnesses, and systematic evaluation
Lead integration design across Salesforce (Flows, Apex, Agentforce/Einstein), Oracle applications, ServiceNow/Zendesk, MuleSoft/Workato, and internal APIs
Build the reusable connector library and self-serve intake path so new use cases onboard without bespoke work every time
Own CI/CD for AI workloads: automated eval gates, model and prompt versioning, deployment orchestration, and rollback strategy
Keep production AI observable and reliable through monitoring, alerting, and data integrity practices (Datadog, Splunk, or equivalent)
Define AI engineering standards for the CX organization: coding patterns, RAG design, eval practices, integration patterns, and documentation
Mentor AI, automation, and prompt engineers through design reviews, pair engineering, and structured feedback
Partner with Enterprise Architecture and governance review boards so systems meet security, privacy, and AI ethics requirements. Identify and mitigate model bias, and handle PII and prompt injection risk deliberately
Present architectures, trade-offs, and ROI clearly to senior leadership and non-technical partners