Principal Applied AI Engineer

New

AppianAI automation

Remote (US)Full-TimePrincipal

Salary$200,000 — $350,000 USD

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Job Details

Experience: 10+ years of relevant software development experience with a BS (or 8+ years of experience paired with a Master of Science in Computer Science or related field).
Required Skills: RESTful APIsDistributed Systems

Hold a Bachelor of Science degree in Computer Science or a related technical/analytical discipline; equivalent experience is not accepted in lieu of a degree.
Have 10+ years of relevant software development experience with a BS, or 8+ years of experience with a Master of Science in Computer Science or a related field.
Bring deep hands-on experience designing and operating production LLM systems.
Demonstrate expertise in code generation, agents, retrieval, structured generation, tool use, model routing, evaluation, and observability.
Have experience using AI coding assistants and evaluating, coaching others on, and selectively applying AI-generated code in production engineering.
Have strong foundations in distributed systems, APIs, security, and reliability.
Demonstrate the ability to contribute at a senior individual-contributor level in distributed systems, APIs, security, and reliability.
Have experience driving technical decisions and shaping engineering practices beyond a single team or project.
Bring experience developing, optimizing, and maintaining high-volume, mission-critical production services.
Be able to communicate technical architectures verbally, visually, and in writing to diverse engineering audiences.
Compilers, program analysis, formal or executable specifications, or legacy modernization experience is preferred.
Experience with enterprise platforms such as Salesforce or ServiceNow is valued.

Architect, build, and optimize high-performance production LLM systems.
Spearhead platform modernization and code refactoring to keep the codebase and AI systems modern and performant.
Use AI coding tools to generate, review, and critically evaluate AI-assisted code.
Drive technical story breakdowns, acceptance criteria, and architecture across complex, multi-tier applications.
Build prototypes to de-risk novel AI approaches and establish technical standards.
Improve availability, latency, scalability, and efficiency through reliability engineering and system tuning.
Promote development best practices, review designs and implementations, and build automation frameworks to prevent recurring problems.
Coach and mentor engineers, identify skill gaps, and support their technical growth.
Share domain knowledge across the department and publish technical documentation.

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