Forward Deployed Solution Engineer – Applied AI

ServiceNow

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

Posted 11/7/2026full-timeRemote • Georgia • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building end-to-end GenAI and machine learning systems, with a strong focus on user value and production deployment. Proficient in backend and frontend technologies, capable of leading implementations and enhancing engineering quality across teams.

Highest-signal resume keywords

Machine Learning/GenAI Systems DevelopmentBackend Development with Python, Node.js, or JavaFrontend Development with React or AngularAWS, Azure, or GCP Deployment with CI/CDRAG Architecture and LLM Pipeline Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenAI SystemsBackend DevelopmentFrontend DevelopmentREST APIGraphQL APIDebugging Distributed SystemsInfrastructure-as-CodePrompt ChainingVector Search

Soft Skills

Customer PartnershipTechnical Relationship ManagementProduct SensibilityUser Value FocusAgile Collaboration

Tools & Technologies

LangChainSemantic KernelAWSAzureGCP

Industry Keywords

SaaS PlatformsServiceNowSalesforceSecure 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 and translate between business stakeholders and engineering
Build backend services, LLM pipelines, and front-end integrations
Deliver solution-ready builds in agile sprints
Author libraries, prompts, and scaffolds that support multiple deployments and projects
Shape internal tooling and platform roadmap primitives
Lead implementations from zero to one
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
Ability to design and implement AI-native software in production environments
Backend experience with Python, Node.js, or Java
Frontend experience with React or Angular
REST/GraphQL API experience
Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management
Experience debugging distributed systems, tuning 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 such as 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

Flexible or remote work persona
Opportunity to work on transformative enterprise AI solutions with strategic customers
Opportunity to shape internal tooling and the Now AI Platform roadmap
Opportunity to author reusable libraries, prompts, and scaffolds
Opportunity to contribute to shared SDKs and developer experience tooling