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 build reusable AI-native solutions for the Now AI Platform. The team embeds in real-world customer environments to address mission-critical challenges and turn successful implementations into reusable patterns.

As a GenAI/ML Engineer, you will work alongside customers to translate ambiguous business problems into production AI systems. You will own end-to-end implementation, from backend services and LLM pipelines to front-end integrations, and deliver solution-ready builds through agile sprints. The role combines customer partnership, software engineering, and AI/ML development to produce resilient, secure, scalable systems and reference implementations for enterprise GenAI.

Success includes converting solutions into scaled production deployments, creating reusable libraries and scaffolds, influencing internal tooling and the platform roadmap, delivering quickly without compromising reliability, and leading implementations from initial concept through delivery. Travel of up to 30% is required to work onsite with customers.

Requirements

• Experience leveraging or critically evaluating how to integrate AI into work processes, decision-making, or problem-solving; examples include AI-powered tools, workflow automation, analysis of AI-driven insights, or exploring AI’s potential impact.
• At least 8 years of software engineering experience, including at least 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.
• 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.
• Skill debugging distributed systems, tuning latency, and implementing monitoring.
• Ability to contribute to shared SDKs and tools to 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%.

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/ML 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.
• Author reusable libraries, prompts, and scaffolds that support multiple deployments and projects.
• Shape internal tooling and contribute to platform roadmap and primitives.
• Lead implementation from zero to one with architects, product managers, and customer teams.
• Travel up to 30% to embed onsite with customers and deliver solutions.

What This Job Offers

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