It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
You will design, build, and operate production-grade agentic AI systems embedded across ServiceNow's platform — autonomous agents that reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale. You will build the substrate that connects all four: SENSE (any data) → DECIDE (any AI model) → ACT (any workflow) → GOVERN (identity + governance). The architectural inflection point is now. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems
- Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes
- Production-grade Python. Systems language (Go, Java, or C++) is a plus
- Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings
- Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics
- Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices
More about this role
AI Engineering and Delivery is the customer-obsessed engineering group building the agentic AI and enterprise-scale search systems that power Now Assist, AI Agents, and the AI-driven experiences our customers rely on every day. We build AI as foundational platform infrastructure — prioritizing robustness, performance, safety, and real-world customer impact at scale.
You will design, build, and operate production-grade agentic AI systems embedded across ServiceNow's platform — autonomous agents that reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale.
- Agentic architecture. Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production, not in notebooks.
- Enterprise-grounded reasoning. Build agents that leverage ServiceNow's data layer — CMDB, Workflow Data Fabric, and Knowledge Graph — to make decisions with context no frontier model has on its own.
- Trust, safety, and governance. Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale.
- Retrieval and...
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