Forward Deployed Solution Engineer – Applied AI FDE

ServiceNow

3d

About The Position

ServiceNow’s Applied AI Forward Deployed Engineering team partners with strategic customers to identify high-value opportunities, accelerate business outcomes, and build reusable AI-native solutions. The team embeds in customer environments to address mission-critical challenges and turn successful solutions into patterns that can scale across the Now AI Platform and enterprise.

The Forward Deployed Solution Engineer owns end-to-end delivery of production-ready AI solutions, from backend services and LLM pipelines to front-end integrations. Working alongside customers in agile sprints, the engineer designs, implements, deploys, and iterates on solutions in live environments. The role also codifies reusable assets, improves developer experience, and provides field-tested feedback to platform and product teams.

Success means delivering resilient, secure, production-grade solutions that can scale into deployments; authoring reusable libraries, prompts, and scaffolds; influencing platform tooling and roadmap; and leading implementation from initial concept through delivery. Travel of up to 30% is required to work onsite with customers.

Requirements

• 10+ years of software engineering experience, including 2+ years building systems in customer-facing or embedded roles.
• Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving; examples may include AI tools, workflow automation, AI-driven insights, or exploring AI’s potential impact.
• Demonstrated ability to design and implement AI-native software in production environments.
• Backend engineering strength in Python, Node.js, or Java; frontend strength in React or Angular; and experience with REST or GraphQL APIs.
• Foundational knowledge of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, and SQL.
• Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management.
• Skill debugging distributed systems, tuning for latency, and implementing monitoring.
• Ability to contribute to shared SDKs and tools and improve engineering velocity across an organization.
• Product sensibility, including prioritization for user value, MVP iteration, and long-term scale.
• Experience deploying in AWS, Azure, or GCP using CI/CD, containers, and infrastructure as code.
• Ability to travel up to 30% for onsite customer work.

Nice To Haves

• Experience integrating AI into SaaS platforms such as ServiceNow or Salesforce.
• Track record of production deployments in secure, regulated enterprise environments.
• Contributions to developer-experience tooling, frameworks, or reusable AI scaffolds.

Responsibilities

• Build solution-ready LLM-enabled applications spanning backend logic, data orchestration, and front-end user interfaces.
• Work alongside customers in the field to adapt, deploy, and iterate on solutions in live environments.
• Deliver production-ready solutions through agile, end-to-end sprints.
• Engineer APIs, orchestration pipelines, vector databases, LLM frameworks, and UI components.
• Integrate with legacy systems, navigate ambiguity, and deliver safely at speed.
• Build reusable scaffolds, SDKs, libraries, prompts, and documentation to scale success across customers.
• Share field-tested insights and extensible code with platform and product teams to inform product strategy and tooling.
• Debug distributed systems, tune performance and latency, and implement monitoring.
• Lead implementation from initial concept through production delivery in collaboration with architects, product managers, and customer teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed