Divisional Artificial Intelligence Engineer III #DK

About the role

SUMMARY Responsible for leading a substantial technical domain within the AI platform and makes it dependable for use across multiple products. Independently translates ambiguous needs into architecture and staged delivery, then remains accountable for operational performance and adoption. Raises engineering quality through technical guidance, mentorship, and practical standards for shared AI systems. ESSENTIAL DUTIES AND RESPONSIBILITIES Owns architecture, implementation, and operational health for a platform domain, such as agent lifecycle and state, sandbox context exchange, model orchestration, or tracing and evaluation. Designs services and interfaces that support long-running tasks across deployments, restarts, retries, inspection, and replay, with appropriate durability and failure recovery. Establishes reusable patterns for context packaging and retrieval across execution environments, including clear boundaries for access, retention, and data movement. Builds or evolves model routing and orchestration capabilities; defines measurements for quality, latency, reliability, and cost per task and uses them to guide improvements. Leads technical discovery with product teams, identifies common needs, and prioritizes platform work that reduces duplicated engineering and unplanned interruptions. Defines domain-level testing, tracing, evaluation, and operational readiness practices; reviews production signals and leads resolution of complex incidents in the domain. Produces architecture documentation and migration guidance that enable engineers to adopt platform capabilities without extensive one-to-one support. Mentors engineers, reviews designs and code, and contributes to technical planning across the AI engineering group. Performs all duties in accordance with all company policies and procedures, and all federal, state and local laws, wherein the Company operates. Performs other duties as assigned. QUALIFICATIONS EDUCATION/EXPERIENCE 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. SKILLS 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 Originally posted on Himalayas