AI Engineer

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

For over 20 years, Smartsheet has helped people and teams achieve–well, anything. As the Intelligent Work Management Platform, we are redefining the velocity of work by uniting people, data, and AI to move businesses forward. We don’t just automate tasks; we eliminate execution silos and turn strategic vision into measurable enterprise impact. We’re creating a space to think big and take action, because when challenge meets purpose and AI-powered execution meets human ingenuity, that’s magic at work. It’s what we show up for every day, helping organizations not just keep up with change, but thrive because of it.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for a Principal AI Engineer, who craves variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. 

You Will:

• Own the target-state architecture for Smartsheet's AI platform, defining the standards that bind Data, Agents, Knowledge Graph, Context Graph into one coherent system.
• Set the architecture for the agent harness and sub-agent framework including orchestration, lifecycle, hand-off contracts, Evals (Offline and Online) and capability-tier classification used across all agent teams.
• Design and own schema/ontology, ingestion and freshness pipelines, and the retrieval and grounding patterns that keep agent responses reliable.
• Build and maintain a reference agent framework that teams fork or extend, so no team re-solves orchestration, tracing, or deployment from scratch.
• Author and publish a shared skills and tools library with clear interface contracts and a versioning model any team can build on.
• Stand up fast, slim prototypes to de-risk architecture decisions (new sub-agent patterns, retrieval strategies, partner capabilities) before teams commit a roadmap to them, and own the criteria for promoting a prototype into a supported, shared capability.
• Define deployment, security, and governance patterns, including the standard path from prototype to production and the AI governance plane applied across platforms and agents.