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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. 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 settings
• Designing and implementing AI-native software architectures that balance innovation with resilience and security
• Codifying reusable assets—libraries, prompts, 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
• Delivering production-grade solutions in agile end-to-end sprints, integrating with legacy systems while maintaining velocity and safety
• Influencing ServiceNow's platform strategy through extensible code and insights from real-world deployments
Success in this role means consistently converting solution builds into scaled production deployments, authoring reusable patterns that power multiple projects, and earning trust from architects, product managers, and customer teams to lead implementations from zero to one.
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, and context management
• Expertise in debugging distributed systems, tuning for latency, and implementing observability/monitoring
• 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 that raise engineering velocity
• Product sensibility: prioritize for user value, MVP iteration, and long-term scalability
• Ability to travel up to 30% for onsite customer engagement
• Experience leveraging or critically thinking about AI integration into work processes, decision-making, and problem-solving
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
