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ServiceNow's Applied AI Forward Deployed Engineering (FDE) team partners with strategic enterprise customers to design, build, and deploy AI-native solutions that solve mission-critical business challenges. As a Senior Forward Deployed Solution Engineer, you will act as the technical leader (CTO of the build) for customer engagements, owning the full stack from backend services and LLM pipelines to front-end integrations.
You will work embedded with customer teams in the field, translating high-value opportunities into production-ready, scalable AI solutions delivered in agile sprints. Your responsibilities include:
• Building solution-ready LLM-enabled applications spanning backend logic, data orchestration, and UI
• Operating in customer environments, adapting and iterating solutions in live production contexts
• Designing and implementing AI-native software architectures that are resilient, secure, and scalable
• Codifying reusable assets—libraries, prompt templates, scaffolds, and SDKs—to accelerate future engagements and scale success across the customer base
• Shaping developer experience by providing field-tested feedback to platform and product teams
• Debugging distributed systems, optimizing for latency, and implementing observability across complex deployments
• Integrating with legacy enterprise systems while maintaining agility and shipping safely at speed
• Contributing to shared tooling and frameworks that raise engineering velocity across the organization
Success is measured by production-grade solution builds that convert to scaled deployments, reusable assets that power multiple projects, influence on platform roadmap and primitives, and your ability to lead implementation from zero to one in high-stakes customer contexts.
REQUIREMENTS:
• 10+ years of software engineering experience, including 2+ years in customer-facing or embedded roles
• Proven ability to design and implement AI-native software in production environments
• Strong backend engineering (Python, Node.js, Java), frontend (React, Angular), and API design (REST/GraphQL)
• Hands-on experience with LLM tooling: LangChain, Semantic Kernel, prompt chaining, vector search, context management
• Expertise in distributed systems debugging, performance tuning, and monitoring/observability
• DevOps fluency: AWS, Azure, or GCP deployment experience with CI/CD, containers, and infrastructure-as-code
• Platform mindset: ability to contribute to shared SDKs and tools
• Product sensibility: prioritize for user value, MVP iteration, and long-term scale
• Experience leveraging or critically thinking about AI integration into work processes, decision-making, or problem-solving
• Willingness to travel up to 30% for onsite customer engagement
PREFERRED:
• Experience integrating AI into SaaS platforms (ServiceNow, Salesforce, or similar)
• Track record of production deployments in secure, regulated enterprise environments
• Contributions to developer experience tooling, frameworks, or reusable AI scaffolds
