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 Solution 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 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

Experience

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), and APIs (REST/GraphQL).

Machine Learning

Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, and SQL.

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 organization.

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 infrastructure-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 developer experience tooling, frameworks, or reusable AI scaffolds

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. 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.