Divisional Artificial Intelligence Engineer III
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Job Description
AI platform engineer leading architecture, reliability, and adoption for shared LLM infrastructure. Building reusable orchestration, observability, and production services across products.
Responsibilities:
- Own architecture, implementation, and operational health for a platform domain, such as agent lifecycle and state, sandbox context exchange, model orchestration, or tracing and evaluation
- Design services and interfaces supporting long-running tasks across deployments, restarts, retries, inspection, and replay, with durability and failure recovery
- Establish reusable patterns for context packaging and retrieval across execution environments, including boundaries for access, retention, and data movement
- Build or evolve model routing and orchestration capabilities; define and use measurements for quality, latency, reliability, and cost per task
- Lead technical discovery with product teams and prioritize platform work that reduces duplicated engineering and unplanned interruptions
- Define domain-level testing, tracing, evaluation, and operational readiness practices; review production signals and lead resolution of complex incidents
- Produce architecture documentation and migration guidance enabling engineers to adopt platform capabilities
- Mentor engineers, review designs and code, and contribute to technical planning across the AI engineering group
- Perform duties in accordance with company policies and applicable federal, state, and local laws
- Perform other duties as assigned
Requirements:
- Bachelor's Degree in Computer Science, Software Engineering, a related technical field, or equivalent combination of education and work experience
- Five (5) or more years of software engineering experience, or equivalent practical experience, including ownership of production services
- Experience building LLM-powered applications or AI infrastructure
- Advanced proficiency in TypeScript and/or Python and experience designing production services and APIs
- Knowledge of distributed systems, asynchronous job processing, persistence, observability, cloud deployment, and secure system design
- Experience shipping and supporting LLM features, including practical tradeoffs among model behavior, latency, and cost
- Ability to lead technical work from ambiguous problem definition through design, implementation, rollout, and operational improvement
- Ability to make platform interfaces reusable and understandable across teams with different product needs
- Ability to influence technical decisions, communicate risk and tradeoffs, and mentor engineers across experience levels
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