Forward Deployed Solution Engineer – Applied AI FDE

About the Role

ServiceNow's Applied AI Forward Deployed Engineering (FDE) team partners with strategic customers to shape the future of enterprise AI. The team identifies high-value opportunities, accelerates business outcomes, and builds reusable AI-native solutions that advance the Now AI Platform. The mission is to partner deeply with customers to build intelligent, scalable AI solutions that solve mission-critical challenges, embedding in real-world complexity to deliver fast, iterate with purpose, and transform successes into reusable patterns.

Why This Role Matters

Enterprises are raising the bar—AI initiatives must deliver business value, not just promise potential. This means taking cutting-edge LLM capabilities and turning them into resilient, secure, and scalable software. As a Senior 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 to design, implement, and deliver solution-ready builds in agile sprints. Your software becomes the reference implementation for scalable GenAI in the enterprise, codifying patterns, shaping internal tooling, and 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—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 debug systems, context, and customer pain points.

What You Will Build

• Solution-ready LLM-enabled applications spanning 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 solutions 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

• AI Integration Experience: Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving—including using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact
• Relevant Experience: 10+ 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

Pay

Base pay of $201,300 - $352,300, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Individual total compensation varies based on factors such as qualifications, skill level, competencies, and work location.

Benefits

• Health plans, including flexible spending accounts
• 401(k) Plan with company match
• ESPP
• Matching donations
• Flexible time away plan and family leave programs