Forward Deployed Solution Engineer (Applied AI FDE)

• 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

• 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 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

• 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 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

Benefits

• Generous family leave

• Matched donations

• Annual learning stipends

• Flexible PTO

• Competitive retirement plan

• Paid volunteer time- 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 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

• Platform influence: Your work shapes internal tooling and is integrated into platform roadmap and primitives

• Reusable impact: You author libraries, prompts, and scaffolds that power multiple deployments and projects

• Engineering leadership: You are trusted by architects, PMs, and customer teams to lead implementation from zero to one

• Velocity and precision: You move fast without breaking things—shaping resilient, secure systems in high-stakes contexts

• Production-grade delivery: Your solution builds consistently convert to scaled deployments in production environments

• Product sensibility: Prioritize for user value, MVP iteration, and long-term scale

• Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL)

• Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring

• Field readiness: Able to travel up to 30% to embed onsite and deliver where it matters

• Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org

• 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

• LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management

• System architecture: Proven ability to design and implement AI-native software in production environments

• DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code

• Relevant Experience: 10+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles

• 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