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Zip

Interest-free BNPL payments provider internationally

Senior AI Engineer - Model Selection and Orchestration

AI Application EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A minimum of 10 years of related experience with a bachelor's degree, or 6 years of experience with a master's degree, or a PhD with 3 years of experience, or equivalent experience.
  • At least 10 years of professional experience building, testing, deploying, and operating production software, including hands-on delivery of AI- or machine-learning-enabled applications beyond prototypes.
  • Strong experience with Python or TypeScript and modern software engineering practices, including API design, automated testing, distributed services, and debugging across application, model, and tool boundaries.
  • Practical experience building applications using large language model tool calling, structured outputs, retrieval-augmented generation, and agent or workflow orchestration.
  • Ability to evaluate classification quality, uncertainty, fallback strategies, and different model or system approaches using measurable outcomes such as task success, latency, reliability, and total inference cost.
  • Experience with cloud deployment, automated testing, continuous integration and continuous delivery, observability, identity and access controls, secrets management, retries, idempotency, and failure recovery in production systems.
  • Strong judgment about when to use deterministic software, retrieval, a model, or a combination of approaches, and when an agent should ask for clarification or hand work to a person.
  • Ability to independently deliver complex technical work within shared architecture standards while collaborating with engineering, Product, Security, and Risk teams; communicate tradeoffs clearly; contribute code reviews; and strengthen engineering practices across the team.
  • Hands-on experience using AI-assisted development tools in day-to-day work to develop, debug, test, evaluate, and improve software, while applying engineering judgment to AI-generated solutions.

Responsibilities

  • Design and implement the decision layer that determines how an agent should respond to a task, including when to use deterministic logic, retrieval, smaller models, reasoning models, tools, or human escalation.
  • Develop intent classification and clarification capabilities that distinguish information requests from actions, identify ambiguous requests, evaluate confidence, and determine when more information or escalation is required.
  • Benchmark rules, retrieval approaches, classifiers, smaller models, and reasoning models against representative tasks; use measurable outcomes such as task success, quality, latency, safety, and total inference cost to recommend model-selection decisions.
  • Implement and improve model adapters, routing logic, structured outputs, timeouts, retry strategies, fallback behavior, and observable failure handling; build testable orchestration that avoids unnecessary dependency on any single model provider.
  • Connect agents to approved APIs and tools with appropriate authentication, authorization, policy checks, and customer confirmation; design for duplicate requests, idempotency, recovery, and other failure scenarios when agents perform state-changing actions.
  • Partner with Security and Risk to implement approved input and output protections, sensitive-data handling, prompt-injection defenses, and controlled access; maintain separation between model judgment and permissions required to execute actions.
  • Develop reusable orchestration patterns, model adapters, routing components, and tooling for domain engineering teams and agent builders.
  • Partner with evaluation engineering to test changes before release, monitor system behavior, investigate failures, and improve model selection and orchestration using production evidence.
  • Use AI-assisted development tools to accelerate development, troubleshoot problems, improve code quality, and write and maintain tests; evaluate AI-generated solutions for correctness, security, performance, and maintainability.
  • Write production code, evaluate AI system behavior, integrate models and tools, contribute to technical decisions, and work with Principal and Staff Engineers, Product, domain engineering, Security, and Risk.

Desired Qualifications

  • Experience with Google Cloud, Google ADK, Model Garden, or equivalent managed AI platforms; semantic routing; open-weight models; Model Context Protocol; reusable agent-development frameworks; Azure integrations; or payments, fintech, or other environments where correctness, auditability, and controlled access are essential.

About the Company

Zip

Interest-free BNPL payments provider internationally

Zip Co provides buy now, pay later (BNPL) services that let people buy things now and pay over time without interest. It operates in markets including Australia, New Zealand, the United Kingdom, the United States, Canada, and Mexico, serving both individual consumers and businesses. Its main products are Zip Pay (for everyday purchases) and Zip Money (for larger expenses). Revenue comes from merchant fees, late fees, and interest on certain products. Zip partners with retailers to offer convenient, transparent payment options that help customers manage their finances, with a focus on simple terms and clear due dates. The company aims to expand globally and grow its market presence by providing flexible, easy-to-use payment solutions that simplify the purchasing process.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Sydney NSW, Australia
Founded
2013

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