Forward Deployed Solution Engineer – Applied AI FDE

ServiceNow's Applied AI Forward Deployed Engineering (FDE) team partners with strategic customers to build intelligent, scalable AI solutions. The Forward Deployed Solution Engineer (FDSE) acts as the CTO of the build, owning everything from backend services to LLM pipelines and front-end integrations, partnering with customers in the field to design, implement, and deliver solution-ready builds in agile sprints. The role codifies patterns, shapes internal tooling, and produces reference implementations for scalable GenAI in the enterprise.

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 such as libraries, prompts, and 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 across APIs, orchestration pipelines, vector DBs, LLM frameworks, and UI components; Integrate with legacy systems, navigate ambiguity, and ship safely at speed; Build scaffolds, SDKs, and documentation to scale success across customers; Inform platform product strategy through field-tested insights and extensible code

Requirements

10+ years of software engineering experience; 2+ years building systems in customer-facing or embedded roles; Proven ability to design and implement AI-native software in production environments; Strength in backend (Python, Node.js, Java), frontend (React, Angular), and APIs (REST/GraphQL); Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL; Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management; Skilled in debugging distributed systems, tuning for latency, and implementing monitoring; Can contribute to shared SDKs and tools; Prioritize for user value, MVP iteration, and long-term scale; Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code; Able to travel up to 30% to embed onsite; Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving

Nice to have

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