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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 (CTO of the build) for 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 architect and deploy production-ready, AI-powered workflows at scale. 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 settings
• Designing and implementing AI-native software systems that are resilient, secure, and scalable across industries
• Codifying reusable assets—libraries, prompt templates, scaffolds, and SDKs—to accelerate future customer engagements
• Debugging distributed systems, diagnosing root problems, and architecting workflows that run reliably at scale
• Shaping developer experience by providing field-tested feedback to platform and product teams
• Influencing internal tooling and platform roadmap through extensible code and production insights
Success in this role means delivering production-grade solutions that consistently convert to scaled deployments, authoring reusable libraries and patterns that power multiple projects, moving fast without breaking things in high-stakes contexts, and earning trust from architects, PMs, and customer teams to lead implementations from zero to one.
The role requires up to 30% travel to embed onsite with customers.
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)
• Foundational knowledge of ML tools and frameworks: MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL
• Familiarity with LLM tooling: LangChain, Semantic Kernel, prompt chaining, vector search, context management
• Expertise in debugging distributed systems, tuning for latency, and implementing monitoring/observability
• DevOps fluency: experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code
• Platform mindset with 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 integrating AI into work processes, decision-making, or problem-solving
PREFERRED:
• Experience integrating AI into SaaS platforms like ServiceNow or Salesforce
• Track record of production deployments in secure, regulated enterprise environments
• Contributions to developer experience tooling, frameworks, or reusable AI scaffolds
