Lead Applied AI Engineer

Appian Corporation

26d•$150,000 - $300,000•Remote

About The Position

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

Requirements

Bachelor of Science degree in Computer Science or a related technical/analytical discipline.
7+ years of relevant software development experience with a BS (or 5+ years of experience paired with a Master of Science in Computer Science or a related field).
Strong distributed-systems background, including retries, idempotency, partial failure, long-running tasks, and recovery.
Strong software engineering, data-pipeline, and observability skills, comfortable owning a production service end to end.
Demonstrated experience using AI coding assistants and a strong ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context.
Exceptional ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences.

Nice To Haves

Expertise in code search, repository intelligence, or developer tools.
Experience with legacy codebase and workflow modernization.
Experience with enterprise platforms such as Salesforce or ServiceNow.

Responsibilities

Architect, build, and optimize production AI systems while keeping a strong hands-on technical presence.
Design reliable agents and orchestration — planning, tool use, memory, and state — with safe, permission-aware boundaries.
Derive requirements and executable specs from source, metadata, tests, and runtime behavior, and map them onto Appian's object model.
Build the semantic and dependency models and retrieval that give AI accurate context across large application estates.
Build the evaluation and CI pipelines that gate quality and catch real regressions.
Use AI coding tools fluently to ship faster without compromising quality or correctness.
Engineer reliability, latency, and efficiency into core systems at enterprise scale.
Coach and guide junior engineers to accelerate their technical growth.
Share expertise across the department and publish to Engineering's permanent documentation site.