Forward Deployed Solution Engineer – Applied AI FDE

ServiceNow • Atlanta, GA

1d

About The Position

ServiceNow’s Applied AI Forward Deployed Engineering team partners with strategic customers to identify high-value opportunities, accelerate business outcomes, and develop reusable AI-native solutions for the Now AI Platform. The team builds intelligent, scalable AI solutions for customers’ mission-critical challenges, turning real-world deployments into reusable patterns that support broader enterprise transformation.

The Forward Deployed Software Engineer owns the technical build, from backend services and LLM pipelines to frontend integrations. Working alongside customers, the engineer turns ambiguous business problems into production-ready AI systems, delivering through agile sprints. The role involves designing and implementing end-to-end GenAI and machine learning solutions, rapidly prototyping and iterating with users, and hardening pilots into reliable, monitored services. The engineer also contributes reusable patterns, internal tooling, and platform capabilities, while translating between business stakeholders and engineering teams.

The role requires a systems-minded, product-oriented engineer who can balance rapid delivery with secure, resilient, and scalable software. Travel of up to 30% may be required to work onsite with customers. The work persona is listed as flexible or remote.

Requirements

• Experience leveraging or critically considering how to integrate AI into work processes, decision-making, or problem-solving.
• At least 8 years of software engineering experience, including at least 2 years 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.
• Engineering strength in backend technologies such as Python, Node.js, or Java; frontend technologies such as React or Angular; and REST or GraphQL APIs.
• Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management.
• Ability to debug distributed systems, tune latency, and implement monitoring.
• Ability to contribute to shared SDKs and tools that improve engineering velocity.
• Product sensibility, including prioritizing user value, MVP iteration, and long-term scale.
• Experience deploying in AWS, Azure, or GCP with 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

• 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, translating between business stakeholders and engineering.
• Raise the bar for machine learning depth and engineering quality across the team.
• Author reusable libraries, prompts, and scaffolds that support multiple deployments and projects.
• Shape internal tooling and contribute work that can influence platform roadmaps and primitives.
• Lead implementation from initial concept through delivery, building resilient and secure systems.

What This Job Offers

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