Build the interview around one claim: you turn messy operational workflows into governed, measurable AI systems that people adopt. Your strongest edge is the combination of manager, hands-on builder, process designer, and security-aware operator.
The AI Lead posting frames the job as enterprise AI transformation in IT & Cyber, not as a pure credit-modeling or research role.
| Signal | What it means for you |
|---|---|
| Reports to CIO, works with CTO org | Ask how the CIO/CTO/IT/Security boundary works. Do not assume the org chart. |
| Leads specialized AI Squad | Position yourself as the person who can build operating model, people, tooling, and delivery rhythm. |
| Partners with HR, Finance, Legal, Ops, IT, Security, Engineering, Research, Product | Talk about workflows across departments, not only developer productivity. |
| Evaluates tools, LLMs, automation frameworks | Show build-vs-buy judgment. Include OpenAI, Anthropic, vector DBs, orchestration, Cursor, Workato-like tools. |
| Prioritizes by ROI, feasibility, long-term impact | Lead with measurable business impact, safe rollout, and adoption. |
Pagaya is a public fintech/AI company operating an AI lending network. It integrates through partner origination APIs, uses AI-powered real-time review, and connects lenders with capital markets.
I could not independently verify a reliable public profile for "Maor Saubron" under that spelling. Keep personalization grounded and use the opening minutes to understand his lens.
"Before we go deep, I saw the role is positioned under the CIO and also works closely with the CTO organization. I would love to understand how you see the AI Squad's mandate: is it primarily internal enterprise AI transformation, product/R&D acceleration, or both?"
"I noticed the role sits in IT & Cyber, while Pagaya also has investment leadership because of the capital-markets side of the business. For this role, how should I understand the CIO reporting line and the boundary with the CTO organization?"
| Pagaya need | Your evidence | How to say it |
|---|---|---|
| Build AI squad from zero | Multi-agent systems, SERS, AutoResolver, lifecycle orchestrator, internal tools. | "I can design the operating model, build first paved paths, then hire and coach into it." |
| Org-wide transformation | Engineering, support escalation, hiring evaluation, analytics, Slack/doc sync, dashboards. | "My work is not limited to developer productivity. I apply AI across business workflows." |
| Technical depth | Next.js, TypeScript, PostgreSQL, Redis/BullMQ, ChromaDB, Claude API, Bedrock, MCP, Python, Playwright. | "I can sit with engineers on architecture and executives on ROI." |
| Security and governance | Human review gates, evidence contracts, source-of-truth separation, advisory derived layers. | "My default is auditability, data boundaries, and clear authority contracts." |
| Cross-functional influence | Work across CS, engineering, product owners, support, interviewers, Jira, Slack, leadership metrics. | "The hard part is getting departments aligned on ownership, data access, and success metrics." |
Use for AI leadership, agents, ROI, production accountability, and complex integrations.
Use for knowledge strategy, prioritization, and turning noise into roadmap.
Use for roadmap thinking, governance, squad process, and human approval gates.
Use for cross-functional AI beyond engineering and risk-aware automation.
Use for management maturity and adoption measurement.
Pagaya is already AI-native, but this role is about making AI an organizational capability across departments. That mix of data scale, regulation, business pressure, and real adoption is exactly where I create leverage.
Use a two-axis filter: measurable business impact and implementation/risk feasibility. Score ROI, time-to-value, data sensitivity, integration complexity, auditability, and adoption readiness.
Create an operating model: intake, prioritization, security review, architecture standards, evals, rollout plan, adoption metrics, and ownership.
Buy commodity capabilities when the workflow is standard and security is acceptable. Build when Pagaya has unique data, unique process advantage, or needs deep integration and control.
Start with data classification, access boundaries, retention, logging, and human approval for irreversible decisions. Use approved retrieval sources, strict permissions, audit trails, and red-team tests.
If not direct: "I have not led a Workato rollout specifically, but the underlying patterns are familiar: event-driven workflows, API integrations, identity, retries, observability, and governance."
How do you define the AI Squad mandate: internal productivity, enterprise workflows, R&D acceleration, product enablement, or all of the above?
The role reports to the CIO and works closely with the CTO org. Where do you want the boundary between IT-led AI enablement and product/research AI to sit?
What triggered creating this role now?
What AI tools or workflows are already in use at Pagaya, officially or unofficially?
What are the first departments where you expect measurable AI impact?
How will success be measured after 6 months: adoption, cost savings, cycle time, risk reduction, revenue impact, or something else?
What is the current risk appetite around employee-facing LLM tools touching internal, customer, or financial data?
What would make you say after 90 days: hiring this person changed the slope of AI adoption?
This role likely needs enterprise implementation, governance, automation, and stakeholder leadership.
Say AI removes repetitive context reconstruction and drafting so people can review, decide, and own higher-value work.
Pagaya is finance, lending, fair-lending, compliance, data security, and capital markets.
No reliable public profile was found under that spelling. Ask calibration questions instead.
They explicitly mention third-party tools and Workato. Show build/buy judgment.
Tie examples to operational pain, adoption, risk controls, and measurable value.
Lead with SERS or candidate evaluation. Use situation, task, action, result, then explain the governance pattern.
Before/after cycle time, manual hours saved, quality or error rate, adoption, escalation rate, cost per run, and stakeholder owner satisfaction.
Agents are useful for ambiguous knowledge work. Deterministic stages, gates, evals, and audit trails are needed around them for production workflows.
Look for AI solution engineering, automation/integration skill, product discovery, security judgment, and people who can ship through ambiguity.
Start from department pain, ship visible wins, measure adoption, create champions, and make the safe path easier than shadow AI.
Pick a real example. Emphasize what you changed: data contract, human gate, eval, rollout size, or stakeholder clarity.
"What I can bring here is a rare combination: I can build the first version myself, manage the team that scales it, and work with executives on choosing the right problems."
Use this for your own wording, follow-up questions, and post-call notes.