Forward Deployed Solutions Engineer (Applied AI)

Join ServiceNow's Applied AI Forward Deployed Engineering (FDE) team as a Forward Deployed Solutions Engineer (Applied AI). In this role, you will partner with strategic customers to build intelligent, scalable AI solutions that address their most critical challenges. You will own the entire software stack, from backend services to LLM pipelines and front-end integrations, and work closely with customers to design, implement, and deliver solution-ready builds. Your work will have a significant impact on the Now AI Platform and the broader enterprise.

- Construire des applications habilitées par LLM prêtes à l'emploi qui couvrent la logique backend, l'orchestration des données et l'interface utilisateur frontale. - Travailler sur le terrain, aux côtés des clients, pour adapter, déployer et itérer dans des environnements en direct. - Codifier des actifs réutilisables - bibliothèques, invites, échafaudages - pour accélérer les engagements futurs.

- You are a systems-minded, AI-native engineer who ships real software. You own the full stack—and are equally motivated by elegant APIs, intuitive UIs, and scalable orchestration pipelines. You think like a product-minded CTO, balancing creativity with pragmatism to deliver impact - You embed deeply with customer teams, diagnose root problems, and architect AI-powered workflows that run at scale. You don’t just debug code—you debug systems, context, and customer pain points - DevOps fluency: Experience deploying in AWS, Azure, or GCP with CI/CD, containers, and infra-as-code - Platform mindset: Can contribute to shared SDKs and tools, raising engineering velocity for the whole org - Field readiness: Able to travel up to 30% to embed onsite and deliver where it matters - Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL - Relevant Experience: 10+ years of software engineering, including 2+ years building systems in customer-facing or embedded roles - Performance & observability: Skilled in debugging distributed systems, tuning for latency, and implementing monitoring - Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) - LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management - System architecture: Proven ability to design and implement AI-native software in production environments - Product sensibility: Prioritize for user value, MVP iteration, and long-term scale - Experience: In leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry - Contributions to dev experience tooling, frameworks, or reusable AI scaffolds - Track record of production deployments in secure, regulated enterprise environments - Experience integrating AI into SaaS platforms like ServiceNow or Salesforce