Senior AI Engineer - Model Selection and Orchestration

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must remove boilerplate, navigation, legal, EEO, etc. Must preserve all substantive info. We need to parse the posting. Let's extract sections: - About the role: The first paragraph: "Senior AI Engineer with experience building production AI systems, model orchestration, tool calling, retrieval, and modern software engineering practices to build reliable AI capabilities across customer-facing and internal experiences.Hands-on engineer who owns components of the decision and orchestration layer that determines when agents should use a model, tool, retrieval system, or deterministic workflow, balancing quality, safety, latency, reliability, and cost.Remote-first opportunity for US-based employees with the option to work in-person out of our Manhattan office. Start your adventure with Zip Become a part of Zip's Engineering team and take on exciting challenges at the intersection of AI, software engineering, and financial technology. At Zip, our engineers build systems that move real money and serve millions of customers, making reliability, security, and sound engineering judgment fundamental to how we build. We are seeking a Senior AI Engineer to help turn rapidly evolving AI capabilities into dependable customer experiences and useful tools for our teams. You’ll work within our AI Platforms team, building shared capabilities that support Zia, our customer-facing AI experience, as well as internal agents and AI-powered workflows across Zip. You’ll play a hands-on role in designing and building the decision and orchestration layer that helps agents interpret requests and determine the right execution approach. Depending on the task, that could mean deterministic logic, retrieval, a lightweight classifier, an AI model, a tool call, or a more complex reasoning workflow. You’ll use evidence about task quality, safety, latency, reliability, and cost to recommend and implement those decisions. This is a builder’s role where you’ll write production code, evaluate AI system behavior, integrate models and tools, contribute to technical decisions, and work closely with Principal and Staff Engineers, Product, domain engineering, Security, and Risk. You’ll use managed capabilities within the Gemini Enterprise Agentic Platform (GEAP) and approved cloud tooling, while building Zip-specific orchestration, decision logic, and integrations where needed." That's about the role. - Responsibilities: The list after "Interesting problems you will get to solve" includes bullet points (though not bullet formatted but sentences). We can treat each sentence as a list item. List items: - Build Intelligent Decision and Routing Systems: 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. - Build Intent and Clarification Flows: Develop intent classification and clarification capabilities that distinguish information requests from actions, identify ambiguous requests, evaluate confidence, and determine when additional information or escalation is required. - Evaluate and Select Models: 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. - Build Reliable Model Orchestration: Implement and improve model adapters, routing logic, structured outputs, timeouts, retry strategies, fallback behavior, and observable failure handling. Build orchestration that is testable and avoids unnecessary dependency on any single model provider. - Integrate Models, APIs, and Tools Safely: 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. - Build Secure AI Systems: Partner with Security and Risk to implement approved input and output protections, sensitive-data handling, prompt-injection defenses, and controlled access. Maintain clear separation between model judgment and the permissions required to execute actions. - Build Reusable AI Platform Capabilities: Develop reusable orchestration patterns, model adapters, routing components, and tooling that make it easier for domain engineering teams and agent builders to create reliable AI-powered experiences. - Measure and Improve AI in Production: Partner with evaluation engineering to test changes before release, monitor system behavior, investigate failures, and continuously improve model selection and orchestration using production evidence. - Build with AI: Use AI-assisted development tools as a core part of your engineering workflow to accelerate development, troubleshoot problems, improve code quality, and write and maintain tests. Apply sound engineering judgment when evaluating AI-generated solutions for correctness, security, performance, and maintainability. - Requirements: The "What You Will Bring To The Team" section includes educational background, software engineering experience, Python/TypeScript, Production AI Engineering, Model Evaluation and Selection, Engineering Craftsmanship, AI Engineering Judgment, Ownership and Collaboration, AI-Enabled Engineering, Bonus Points. We can treat these as sub-sections maybe under Requirements or Qualifications. The instruction says use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly. We have educational background etc. Could be Requirements (minimum experience). Qualifications maybe similar. Skills maybe Python/TypeScript etc. Benefits: "What You Will Get In Return" includes flexible working culture, unlimited PTO, parental leave, 401k match, learning stipend, office, insurance, etc. Pay: "The annual base Pay Range for this position is $162,000 - $205,000." Also mention bonus/equity etc. Schedule: Not explicitly given; remote-first opportunity for US-based employees with option to work in-person out of Manhattan office. That could be schedule? It's about location/remote. Could be considered Schedule? Might be better to put under About the role? But we need a Schedule section only if posting actually states schedule. It mentions remote-first, option to work in-person out of Manhattan office. That's about work arrangement, not schedule hours. Could treat as Schedule? The instruction: "never create Pay/Schedule/Benefits sections unless the posting actually states them." It does state remote-first and option to work in-person, which is about work location, not schedule. Might be considered Schedule? Not sure. Safer to not create Schedule section if not explicit about hours. We'll omit Schedule. Benefits: we have details. Pay: we have base pay range and mention bonus/equity. Now we need to output HTML with

for each section heading, and

• for lists,

  for prose. We must not repeat job title as heading. So headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay. Possibly also "What You Will Bring To The Team" could be split into Requirements, Qualifications, Skills. We need to decide. Let's extract: Educational Background: Minimum of 10 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or a PhD with 3 years experience; or equivalent experience. Software Engineering Experience: 10+ years of professional experience building, testing, deploying, and operating production software, including hands-on delivery of AI or ML-enabled applications beyond prototypes. Python or TypeScript: 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. Production AI Engineering: Practical experience building applications using LLM tool calling, structured outputs, retrieval-augmented generation, and agent or workflow orchestration. Model Evaluation and Selection: 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. Engineering Craftsmanship: Experience with cloud deployment, automated testing, CI/CD, observability, identity and access controls, secrets management, retries, idempotency, and failure recovery in production systems. AI Engineering Judgment: Strong judgment about when a problem should be solved with deterministic software, retrieval, a model, or a combination of approaches, as well as when an agent should ask for clarification or hand work to a person. Ownership and Collaboration: Ability to independently deliver complex technical work within shared architecture standards while collaborating effectively with Principal and Staff Engineers, Product, domain engineering teams, Security, and Risk. You communicate tradeoffs clearly, contribute thoughtful code reviews, and help strengthen engineering practices across the team. AI-Enabled Engineering: Hands-on experience using AI-assisted development tools as part of your day-to-day engineering workflow to develop, debug, test, evaluate, and improve software while applying sound engineering judgment to AI-generated solutions. Bonus Points: Experience with Google Cloud, Google ADK, Model Garden, or equivalent managed AI platforms; semantic routing; open-weight models; Model Context Protocol (MCP); reusable agent-development frameworks; Azure integrations; or payments, fintech, or other environments where correctness, auditability, and controlled access are essential. We could categorize: - Requirements: Educational Background, Software Engineering Experience, Production AI Engineering, Model Evaluation and Selection, Engineering Craftsmanship, AI Engineering Judgment, Ownership and Collaboration, AI-Enabled Engineering. - Qualifications: maybe Python/TypeScript? Or could be Skills: Python/TypeScript. But the instruction says use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map. Let's assign: - About the role: as earlier. - Responsibilities: list of interesting problems. - Requirements: educational background, software engineering experience, production AI engineering, model evaluation and selection, engineering craftsmanship, AI engineering judgment, ownership and collaboration, AI-enabled engineering. - Qualifications: maybe Python/TypeScript? Or could be "Bonus Points" as Qualifications? Actually qualifications often include education and experience. But we already used Requirements for those. Could treat Qualifications as "Bonus Points" (preferred but not required). However the instruction says include a section ONLY when posting has real, specific content for it. Bonus Points is specific content. So we could have a Qualifications section for Bonus Points. - Skills: Python/TypeScript. - Benefits: list from "What You Will Get In Return". - Pay: base pay range and mention bonus/equity. - Schedule: not present, omit. Now we need to ensure we don't include filler like "commensurate with experience". Not present. Now produce HTML. We'll wrap each section in

  heading then content. For lists, use

  • . For prose, use

    . We need to preserve all substantive info; not invent or summarize away detail. Let's craft. About the role: combine paragraphs into maybe multiple

    but we can combine into one

    with sentences. Ensure we keep all details. We'll produce:

    About the role

    Senior AI Engineer with experience building production AI systems, model orchestration, tool calling, retrieval, and modern software engineering practices to build reliable AI capabilities across customer-facing and internal experiences. Hands-on engineer who owns components of the decision and orchestration layer that determines when agents should use a model, tool, retrieval system, or deterministic workflow, balancing quality, safety, latency, reliability, and cost. Remote-first opportunity for US-based employees with the option to work in-person out of our Manhattan office. Start your adventure with Zip. Become a part of Zip's Engineering team and take on exciting challenges at the intersection of AI, software engineering, and financial technology. At Zip, our engineers build systems that move real money and serve millions of customers, making reliability, security, and sound engineering judgment fundamental to how we build. We are seeking a Senior AI Engineer to help turn rapidly evolving AI capabilities into dependable customer experiences and useful tools for our teams. You’ll work within our AI Platforms team, building shared capabilities that support Zia, our customer-facing AI experience, as well as internal agents and AI-powered workflows across Zip. You’ll play a hands-on role in designing and building the decision and orchestration layer that helps agents interpret requests and determine the right execution approach. Depending on the task, that could mean deterministic logic, retrieval, a lightweight classifier, an AI model, a tool call