Forward Deployed Solution Engineer – Applied AI

ServiceNow

. Partner directly with customers to discover problems and frame high-impact AI use cases .

Posted 11/3/2026full-timeRemote • New York • United StatesSeniorLead💰 $176,100 - $308,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing end-to-end GenAI and machine learning systems, with a strong focus on user value and production readiness. Proficient in backend and frontend technologies, cloud deployment, and agile methodologies to deliver high-impact AI solutions.

Highest-signal resume keywords

Machine Learning/GenAI Systems DevelopmentBackend Development with Python, Node.js, and JavaFrontend Development with React and AngularExperience with AWS, Azure, or GCP DeploymentPrototyping and Iterating with Real Users

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenAI SystemsRAG ArchitecturesPythonNode.jsJavaReactAngularREST/GraphQL APIsDebugging Distributed Systems

Soft Skills

Technical Relationship ManagementUser-Centric DesignCollaboration with StakeholdersProblem-SolvingAgile Methodologies

Tools & Technologies

LangChainSemantic KernelVector DatabasesCI/CDInfrastructure-as-CodeContainersMonitoring ToolsDeveloper-Experience ToolingPrompt ChainingContext Management

Industry Keywords

AI IntegrationSaaS PlatformsProduction DeploymentsSecure EnvironmentsRegulated Environments

Tech Stack

Tools & technologies

AngularAWSAzureDistributed SystemsGoogle Cloud PlatformGraphQLJavaJavaScriptNode.jsPythonReactServiceNow

About the role

Key responsibilities & impact

Partner directly with customers to discover problems and frame high-impact AI use cases
Design and build end-to-end GenAI/ML systems, including RAG pipelines, agentic workflows, evaluation, and guardrails
Ship AI systems to production
Prototype quickly, iterate with real users, and harden pilots into reliable, monitored services
Own the technical relationship between business stakeholders and engineering
Translate between business stakeholders and engineering
Author libraries, prompts, and scaffolds that power multiple deployments and projects
Shape internal tooling and contribute to platform roadmap and primitives
Lead implementation from zero to one
Deliver solution-ready builds in agile sprints alongside customers
Raise ML depth and engineering quality across the team

Requirements

What you’ll need

8+ years of software engineering experience
2+ years building systems in customer-facing or embedded roles
Experience building machine learning/GenAI systems with LLMs, embeddings, vector databases, and RAG architectures
Proven ability to design and implement AI-native software in production environments
Backend experience with Python, Node.js, and Java
Frontend experience with React and Angular
Experience with REST/GraphQL APIs
Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management
Skilled in debugging distributed systems, tuning for latency, and implementing monitoring
Ability to contribute to shared SDKs and tools
Product sensibility focused on user value, MVP iteration, and long-term scale
Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code
Able to travel up to 30% to embed onsite and deliver where it matters
Experience integrating AI into work processes, decision-making, or problem-solving
Preferred: experience integrating AI into SaaS platforms like ServiceNow or Salesforce
Preferred: track record of production deployments in secure, regulated enterprise environments
Preferred: contributions to developer-experience tooling, frameworks, or reusable AI scaffolds

Benefits

Comp & perks

Equity (when applicable)
Variable/incentive compensation
Health plans
Flexible spending accounts
401(k) Plan with company match
ESPP
Matching donations
Flexible time away plan
Family leave programs