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Salary: USD 201,300 - 352,300 / annual
ServiceNow's Applied AI Forward Deployed Engineering (FDE) team is seeking a Forward Deployed Solution Engineer to partner with strategic enterprise customers and build transformative AI-native solutions. You will act as the technical leader of customer engagements, owning the full stack from backend services and LLM pipelines to front-end integrations. Working embedded with customer teams in the field, you'll design, implement, and deliver production-ready AI solutions in agile sprints, then codify successful patterns into reusable libraries and scaffolds that accelerate future deployments across the platform.
In this role, you will build solution-ready LLM-enabled applications spanning backend logic, data orchestration, and UI; operate in customer environments to adapt, deploy, and iterate in live production settings; codify reusable assets including libraries, prompts, and scaffolds; and shape developer experience by providing field-tested feedback to platform and product teams. You'll deliver production-grade solutions that consistently convert to scaled deployments, author libraries and frameworks that power multiple projects, influence internal tooling and platform roadmap through your work, move with velocity while maintaining resilience and security in high-stakes contexts, and lead implementation efforts from zero to one alongside architects, PMs, and customer teams.
Success means your solution builds convert to production at scale, your reusable patterns power multiple customer deployments, your work shapes platform primitives and tooling, you ship fast without breaking things in regulated environments, and you're trusted to lead complex implementations.
**Requirements:**
- 10+ years of software engineering experience, including 2+ years in customer-facing or embedded roles
- Proven ability to design and implement AI-native software in production environments
- Strong backend skills (Python, Node.js, Java), frontend capabilities (React, Angular), and API design (REST/GraphQL)
- Familiarity with LLM tooling: LangChain, Semantic Kernel, prompt chaining, vector search, context management
- Skilled in debugging distributed systems, tuning for latency, and implementing monitoring/observability
- Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code
- Ability to travel up to 30% for onsite customer engagement
- Experience leveraging or critically thinking about integrating AI into work processes, decision-making, or problem-solving
- Platform mindset with ability to contribute to shared SDKs and tools
- Product sensibility with focus on user value, MVP iteration, and long-term scale
**Preferred Qualifications:**
- Experience integrating AI into SaaS platforms like ServiceNow or Salesforce
- Track record of production deployments in secure, regulated enterprise environments
- Contributions to developer experience tooling, frameworks, or reusable AI scaffolds
