Forward Deployed Solution Engineer – Applied AI FDE

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow—helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. Join us to put AI to work for people.

About the Role

ServiceNow’s Applied AI Forward Deployed Engineering (FDE) team partners 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: partner deeply with 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.

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 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.

Responsibilities

• 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.
• 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.

Requirements

• 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: 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

For positions in this location, we offer a 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 will vary 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.
• Employee Stock Purchase Plan (ESPP).
• Matching donations.
• Flexible time away plan and family leave programs.