Forward Deployed Solution Engineer- Applied AI

ServiceNow ServiceNow · Enterprise · Santa Clara, CA +1 · Product

This role focuses on building and deploying production-ready, LLM-enabled applications for enterprise customers, acting as a full-stack engineer for AI solutions. It involves adapting, integrating, and iterating on AI-native software in live customer environments, codifying reusable assets, and influencing platform development.

What you'd actually do

  1. Build solution-ready LLM-enabled applications that span backend logic, data orchestration, and front-end UI
  2. Operate in the field, working side-by-side with customers to adapt, deploy, and iterate in live environments
  3. Codify reusable assets—libraries, prompts, scaffolds—to accelerate future engagements
  4. Shape developer experience by sharing feedback with platform and product teams
  5. Deliver Production - ready solution in agile end-to-end sprints

Skills

Required

  • 10+ years of software engineering
  • 2+ years building systems in customer-facing or embedded roles
  • System architecture
  • AI-native software in production environments
  • backend (Python, Node.js, Java)
  • frontend (React, Angular)
  • APIs (REST/GraphQL)
  • LangChain, Semantic Kernel, prompt chaining, vector search, and context management
  • Debugging distributed systems
  • Tuning for latency
  • Implementing monitoring
  • Platform mindset
  • Product sensibility
  • DevOps fluency (AWS, Azure, or GCP)
  • CI/CD, containers, and infra-as-code
  • Field readiness

Nice to have

  • integrating AI into SaaS platforms like ServiceNow or Salesforce
  • production deployments in secure, regulated enterprise environments
  • Contributions to dev experience tooling, frameworks, or reusable AI scaffolds

What the JD emphasized

  • AI-native software in production environments
  • LLM capabilities
  • resilient, secure, and scalable software
  • battle-tested in production and scalable across industries
  • production-grade delivery
  • scaled deployments in production environments
  • secure, regulated enterprise environments

Other signals

  • customer-facing AI solutions
  • production-grade delivery
  • scalable GenAI in the enterprise
  • LLM-enabled applications