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ServiceNow's Applied AI Forward Deployed Engineering (FDE) team is seeking a Forward Deployed Solution Engineer to partner with strategic enterprise customers and shape the future of enterprise AI. You will act as the CTO of customer builds, owning the full stack from backend services to LLM pipelines and front-end integrations.
In this role, you will design, implement, and deliver solution-ready AI applications in agile sprints, working embedded with customer teams in the field. You'll build production-grade LLM-enabled applications that span backend logic, data orchestration, and intuitive UIs. Your work will serve as reference implementations for scalable GenAI in the enterprise, and you'll codify reusable patterns—libraries, prompts, scaffolds—that accelerate future engagements and shape internal tooling.
Key responsibilities include:
- Delivering production-ready solutions in end-to-end agile sprints
- Engineering with versatility across APIs, orchestration pipelines, vector databases, LLM frameworks, and UI components
- Operating with agility to integrate with legacy systems, navigate ambiguity, and ship safely at speed
- Building reusable assets and SDKs to scale success across customers
- Informing product strategy through field-tested insights and extensible code
- Traveling up to 30% to embed onsite with customers and deliver impact
Success is measured by production-grade deployments that scale, reusable libraries and scaffolds powering multiple projects, influence on platform roadmap, and your ability to lead implementations from zero to one while maintaining velocity and precision in high-stakes contexts.
QUALIFICATIONS:
- 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 skills (Python, Node.js, Java), frontend expertise (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
- Skilled in debugging distributed systems, tuning for latency, and implementing monitoring
- Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infrastructure-as-code
- Ability to contribute to shared SDKs and tools with a platform mindset
- Product sensibility with focus on user value, MVP iteration, and long-term scale
- Experience leveraging or critically thinking about integrating AI into work processes and decision-making
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
