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ServiceNow's Applied AI Forward Deployed Engineering (FDE) team partners with strategic enterprise customers to design, implement, and deliver AI-native solutions that solve mission-critical business challenges. As a Forward Deployed Solution Engineer, you will act as the technical leader of customer engagements, owning the full stack from backend services and LLM pipelines to front-end integrations.
You will embed directly with customer teams in the field, working in agile sprints to deliver production-ready, battle-tested AI solutions. Your responsibilities include:
• Building solution-ready LLM-enabled applications spanning backend logic, data orchestration, and UI components
• Operating in customer environments, adapting and iterating solutions in live production contexts
• Designing and implementing AI-native software architectures that scale across industries and use cases
• Codifying reusable assets—libraries, prompt templates, scaffolds, and SDKs—to accelerate future engagements and raise organizational velocity
• Shaping developer experience by providing field-tested feedback to platform and product teams
• Debugging distributed systems, optimizing for latency, and implementing comprehensive monitoring and observability
• Integrating with legacy enterprise systems while maintaining security and compliance in regulated environments
• Influencing platform strategy through extensible code and insights from production deployments
Success in this role means consistently delivering production-grade solutions that convert to scaled deployments, authoring reusable patterns that power multiple projects, and earning trust from architects, PMs, 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 ML/AI tooling: MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL
• Familiarity with LLM frameworks and tools: LangChain, Semantic Kernel, prompt chaining, vector search, context management
• Expertise in distributed systems debugging, performance tuning, and observability implementation
• DevOps fluency: AWS, Azure, or GCP deployment experience with CI/CD, containers, and infrastructure-as-code
• Ability to travel up to 30% for onsite customer engagement
• Experience integrating AI into work processes, decision-making, and problem-solving
• Platform mindset with ability to contribute to shared SDKs and developer tooling
• Product sensibility focused on user value, MVP iteration, and long-term scalability
PREFERRED QUALIFICATIONS:
• 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
