Applied AI Engineer

Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you’ll be part of a passionate team dedicated to accomplishing hard things, together.

Lead Applied AI Engineer

Location: Remote (US)

Grow your engineering career building the AI systems that understand, model, and rebuild enterprise applications — powering AI automation for the world's largest enterprises.

ABOUT THE TEAM

Appian Engineering spans the full depth of our platform: from the foundational layers that power enterprise scale, to the AI capabilities redefining what automation can do. We operate in a highly collaborative, fast-paced environment focused on technical precision, continuous learning, and high code quality. By joining our team, you will solve real-world problems that directly shape how Appian delivers our AI-Powered Process Automation platform to enterprises around the world.

THE OPPORTUNITY

As a Lead Applied AI Engineer, you will serve as a technical linchpin for the team building AI systems that reason about enterprise applications end to end — spanning agents and orchestration, specification extraction from existing systems, application intelligence and retrieval, and rigorous LLM evaluation. These skills are shared across the team, so you'll bring production depth in some of these areas and the range to grow into the others. Equipped with cutting-edge AI tooling, you'll design and deliver systems of real sophistication, shape engineering practices, and deepen your expertise while ensuring flawless Enterprise-Grade Orchestration.

WHAT YOU'LL DO

• Develop Clean Software: Architect, build, and optimize production AI systems while keeping a strong hands-on technical presence.

• Build Production Agents: Design reliable agents and orchestration — planning, tool use, memory, and state — with safe, permission-aware boundaries.

• Extract Specifications from Existing Systems: Derive requirements and executable specs from source, metadata, tests, and runtime behavior, and map them onto Appian's object model.

• Model Application Intelligence: Build the semantic and dependency models and retrieval that give AI accurate context across large application estates.

• Prove Correctness with Evaluation: Build the evaluation and CI pipelines that gate quality and catch real regressions.