Forward Deployed Solution Engineer – Applied AI FDE

ServiceNow • New York, NY

1d • $176,100 - $308,200

About The Position

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

The Forward Deployed Software Engineer owns end-to-end solution development, from backend services and LLM pipelines to front-end integrations. Working directly with customers, the engineer translates ambiguous business problems into production-ready AI systems, delivers through agile sprints, and helps establish reference implementations for enterprise GenAI. The role also contributes reusable libraries, prompts, scaffolds, and internal tooling, and informs the platform roadmap.

The successful candidate is a systems-minded, product-oriented engineer who builds full-stack software and balances speed with reliability, security, and scalability. The role may require travel up to 30% for onsite customer work.

Requirements

• Experience leveraging AI or critically considering how to integrate AI into work processes, decision-making, or problem-solving; examples may include AI-powered tools, workflow automation, analysis of AI-driven insights, or exploring AI’s potential impact.
• 8+ years of software engineering experience, including 2+ years building systems in customer-facing or embedded roles.
• Experience building machine-learning or GenAI systems using modern AI/ML technologies, including LLMs, embeddings, vector databases, and RAG architectures.
• Proven ability to design and implement AI-native software in production environments.
• Backend engineering strength with Python, Node.js, or Java; frontend experience with React or Angular; and experience with REST or GraphQL APIs.
• Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management.
• Ability to debug distributed systems, tune for latency, and implement monitoring.
• Ability to contribute to shared SDKs and tools that improve engineering velocity across an organization.
• Product sensibility, including prioritizing user value, MVP iteration, and long-term scale.
• Experience deploying in AWS, Azure, or GCP using CI/CD, containers, and infrastructure as code.
• Able 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

• Partner directly with customers to discover problems and frame high-impact AI use cases.
• Design and build end-to-end GenAI and machine-learning systems, including RAG pipelines, agentic workflows, evaluation, and guardrails, and ship them 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 teams.
• Raise the bar for machine-learning depth and engineering quality across the team.
• Create reusable libraries, prompts, scaffolds, and patterns that support multiple deployments and projects.
• Shape internal tooling and contribute to the platform roadmap and primitives.
• Lead implementation from initial concept through production delivery, balancing velocity with resilient and secure engineering.

Benefits

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

What This Job Offers

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