Full-Time Forward Deployed Solution Engineer – Applied AI FDE
ServiceNow is hiring a remote Full-Time Forward Deployed Solution Engineer – Applied AI FDE. The career level for this job opening is Experienced and is accepting New York, New York based applicants remotely. Read complete job description before applying.
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ServiceNow’s Applied AI Forward Deployed Engineering (FDE) team partners with strategic customers to shape the future of enterprise AI. We identify opportunities, accelerate business outcomes, and build reusable AI-native solutions.
Why This Role Matters: Turn cutting-edge LLM capabilities into resilient, secure, and scalable software. As a Forward Deployed Solution Engineer (FDSE), you act as the CTO of the build, owning backend services, LLM pipelines, and front-end integrations. Partner with customers to design, implement, and deliver solution-ready builds in agile sprints.
Who You Are: A systems-minded, AI-native engineer who ships real software. You own the full stack and balance creativity with pragmatism to deliver impact.
You will:
- Build solution-ready LLM-enabled applications.
- Operate in the field with customers.
- Codify reusable assets.
- Shape developer experience.
What You’ll Do:
- Deliver Production - ready solution in agile end-to-end sprints.
- Engineer with versatility: APIs, orchestration pipelines, vector DBs, LLM frameworks, UI components
- Operate with agility: integrate with legacy systems, navigate ambiguity, ship safely at speed
- Codify patterns: build scaffolds, SDKs, and documentation to scale success across customers
- Influence platform: inform product strategy through field-tested insights and extensible code
What Success Looks Like:
- Production-grade delivery.
- Reusable impact.
- Platform influence.
- Velocity and precision.
- Engineering leadership.
What You Bring:
Experience integrating AI into work processes, decision-making, or problem-solving.
Relevant Experience: 8+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles
System architecture: Proven ability to design and implement AI-native software in production environments
Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL)
LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management
Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring
Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org
Product sensibility: Prioritize for user value, MVP iteration, and long-term scale
DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code
Field readiness: Able to travel up to 30% to embed onsite and deliver where it matters
Preferred Qualifications: Experience integrating AI into SaaS platforms like ServiceNow or Salesforce. Track record of production deployments in secure, regulated enterprise environments. Contributions to dev experience tooling, frameworks, or reusable AI scaffolds