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ServiceNow's Applied AI Forward Deployed Engineering (FDE) team partners with strategic enterprise customers to design, build, and deploy production-grade AI solutions that solve mission-critical business problems. As a Forward Deployed Solution Engineer, you will work at the intersection of customer discovery, AI architecture, and rapid prototyping.
You will lead strategic discovery workshops to identify high-impact AI opportunities, then architect and build end-to-end GenAI/ML systems—including RAG pipelines, agentic workflows, evaluation frameworks, and guardrails. You'll prototype quickly with real users, iterate based on feedback, and harden pilots into reliable, monitored production services. You own the technical relationship, translating between business stakeholders and engineering teams to ensure alignment and drive adoption.
Key responsibilities include:
- Partner directly with customers to discover problems and frame high-impact AI use cases
- Design and build end-to-end GenAI/ML systems and ship them to production within 8–12 weeks
- Prototype rapidly, iterate with real users, and harden solutions into reliable services
- Own the technical relationship, translating between business and engineering stakeholders
- Codify best practices, create repeatable frameworks, and build reusable templates
- Capture field insights from deployments to shape product strategy and platform priorities
- Enable scale by equipping internal teams and customers with documentation and technical resources
- Raise the bar for ML rigor and engineering quality across the team
Success is measured by delivering validated, production-ready AI solutions that drive measurable business outcomes (efficiency, automation, adoption, satisfaction), creating reusable assets leveraged across teams, and becoming a trusted strategic partner to customers and internal stakeholders.
REQUIREMENTS:
- AI Mastery: Demonstrated experience using or critically evaluating AI integration in workflows, decision-making, and problem-solving. This may include AI-based tools, workflow automation, analysis of AI-generated insights, or assessment of AI impact on function or industry.
- Relevant Experience: 8+ years of software engineering, including 2+ years building and deploying systems in customer-facing or embedded roles.
- Applied ML/AI Experience: 3+ years building and deploying end-to-end ML or AI systems in production—from data preparation through deployment, monitoring, and iteration. Ability to reason about model selection and trade-offs between prompting, RAG, and fine-tuning, including knowing when not to use a model.
- LLM Application Development: Hands-on experience with retrieval-augmented generation (RAG), embeddings and vector databases (pgvector, Pinecone, Weaviate, FAISS), prompt engineering and chaining, structured outputs and function/tool calling, context management, and agentic or multi-step workflows.
- Evaluation Rigor: Ability to define success metrics and build evaluation harnesses for non-deterministic systems, including golden datasets, evaluation frameworks, and automated testing.
