Forward Deployed Solution Engineer - Applied AI FDE

About the role

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.

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.

What You'll Do

As a GenAI/ML Engineer on the FDE team, you will

• Forward Deployed Engineers sit alongside our customers and turn ambiguous business problems into working AI systems.
• Partner directly with customers to discover problems and frame high-impact AI use cases.
• Design and build end-to-end GenAI/ML systems - RAG pipelines, agentic workflows, evaluation, and guardrails - and ship them to production.
• Prototype quickly, iterate with real users, and harden pilots into reliable, monitored services.
• Own the technical relationship, translating between business stakeholders and engineering.
• Raise the bar for ML depth and engineering quality across the team.

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. Experience building machine learning/ GenAI systems with modern AI/ML technologies, including LLMs, embeddings, vector databases, and RAG architectures.
• 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

Additional Information: Work Personas

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.