Forward Deployed Solution Engineer – Applied AI FDE

Team Bio:
ServiceNow’s Applied AI Forward Deployed Engineering (FDE) team is where bold ideas meet transformative action. We partner with our most strategic customers to shape the future of enterprise AI. Together, we identify high-value opportunities, accelerate business outcomes, and build reusable AI-native solutions that advance the Now AI Platform.
Our mission: We partner deeply with our customers to build intelligent, scalable AI solutions that solve their most mission-critical challenges. By embedding in real-world complexity, we deliver fast, iterate with purpose, and transform every success into reusable patterns that accelerate transformation across the Now Platform and the broader enterprise.
Why This Role Matters:
Enterprises are raising the bar. AI initiatives must deliver business value—not just promise potential. That means taking cutting-edge LLM capabilities and turning them into resilient, secure, and scalable software.
As a Forward Deployed Software Engineer (FDSE), you act as the CTO of the build—owning everything from backend services to LLM pipelines and front-end integrations. You partner with customers in the field to design, implement, and deliver solution-ready builds in agile sprints. Your software becomes the reference implementation for scalable GenAI in the enterprise. You codify patterns, shape internal tooling, and accelerate innovation—delivering systems that are battle-tested in production and scalable across industries.
Who You Are:
You are a systems-minded, AI-native engineer who ships real software. You own the full stack—and are equally motivated by elegant APIs, intuitive UIs, and scalable orchestration pipelines. You think like a product-minded CTO, balancing creativity with pragmatism to deliver impact.
You embed deeply with customer teams, diagnose root problems, and architect AI-powered workflows that run at scale. You don’t just debug code—you debug systems, context, and customer pain points.
You will:
Build solution-ready LLM-enabled applications that span backend logic, data orchestration, and front-end UI
Operate in the field, working side-by-side with customers to adapt, deploy, and iterate in live environments
Codify reusable assets—libraries, prompts, scaffolds—to accelerate future engagements
Shape developer experience by sharing feedback with platform and product teams
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: Your solution builds consistently convert to scaled deployments in production environments
Reusable impact: You author libraries, prompts, and scaffolds that power multiple deployments and projects
Platform influence: Your work shapes internal tooling and is integrated into platform roadmap and primitives
Velocity and precision: You move fast without breaking things—shaping resilient, secure systems in high-stakes contexts
Engineering leadership: You are trusted by architects, PMs, and customer teams to lead implementation from zero to one
What You Bring:
Experience: In leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry
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

Employment type
Full-time

Work arrangement
Hybrid