Lead Applied AI Engineer
New
A
AppianAI automation
Remote (US)Full-TimeLead
SalaryBase Salary Range $150,000 — $300,000 USD. A discretionary bonus may be awarded in recognition of individual and company performance.
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Job Details
- Experience
- 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).
- Required Skills
- SalesforceSoftware EngineeringDistributed Systems
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.
- Ability to own a production service end to end.
- Demonstrated experience using AI coding assistants.
- Ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context.
- Ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences.
- Preferred: expertise in code search, repository intelligence, or developer tools.
- Preferred: experience with legacy codebase and workflow modernization.
- Preferred: experience with enterprise platforms such as Salesforce or ServiceNow.
Responsibilities
- Architect, build, and optimize production AI systems while maintaining a hands-on technical role.
- Design reliable agents and orchestration for planning, tool use, memory, and state with safe, permission-aware boundaries.
- Derive requirements and executable specifications from source, metadata, tests, and runtime behavior, and map them onto Appian's object model.
- Build semantic and dependency models and retrieval to provide AI context across large application estates.
- Build evaluation and CI pipelines that gate quality and catch regressions.
- Use AI coding tools to ship faster without compromising quality or correctness.
- Engineer reliability, latency, and efficiency into core systems at enterprise scale.
- Coach and guide junior engineers.
- Share expertise across the department and publish to Engineering's permanent documentation site.
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