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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 build production-ready, scalable AI solutions. Your responsibilities include designing and implementing LLM-enabled applications that integrate with legacy systems, orchestrating data pipelines and vector databases, and delivering systems that are battle-tested in production environments. You will codify reusable patterns—libraries, prompts, SDKs, and scaffolds—that accelerate future engagements and scale success across the customer base.
Beyond delivery, you will shape the ServiceNow platform by providing field-tested insights to product and platform teams, informing roadmap decisions and contributing to internal tooling that raises engineering velocity across the organization. You will operate with agility and precision, balancing speed with resilience and security in high-stakes enterprise contexts.
Success in this role means consistently converting solution builds into scaled production deployments, authoring reusable assets that power multiple projects, influencing platform strategy through extensible code and customer insights, and earning trust from architects, product managers, and customer teams as a technical leader capable of driving 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 development (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 including LangChain, Semantic Kernel, prompt chaining, vector search, and context management
- Expertise in debugging distributed systems, tuning for latency, and implementing monitoring and observability
- DevOps fluency with AWS, Azure, or GCP; experience with CI/CD, containers, and infrastructure-as-code
- 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
- Product sensibility with focus on user value, MVP iteration, and long-term scalability
- Platform mindset with ability to contribute to shared SDKs and tools
PREFERRED QUALIFICATIONS:
- Experience integrating AI into SaaS platforms such as ServiceNow or Salesforce
- Track record of production deployments in secure, regulated enterprise environments
- Contributions to developer experience tooling, frameworks, or reusable AI scaffolds
