Engineering Manager (AI)New

About AiPrise

AiPrise is a YC-backed, Series A company building the global compliance infrastructure for financial services. We help banks, fintechs, cross-border payment providers, stablecoin and crypto companies, and marketplaces gain a 360° view of every business they onboard, anywhere in the world.

Through a single integration, we connect 800+ data sources and 80+ verification partners across 200+ countries, powering KYB, KYC, AML, sanctions screening, and ongoing monitoring for companies like Meta, Airwallex, Fireblocks, and Bridge.

Our AI Agents help compliance teams do more with less, resolving more cases, catching more fraud, and scaling operations without scaling headcount.

We're ~60 people, Series A funded, and obsessed with a single problem: making compliance feel simple for the teams who live in it every day.

Borders shouldn't be blockers. Compliance is hard. AiPrise shouldn't be.

About the role

We are hiring an Engineering Manager to lead and grow the team that builds AiPrise's AI agents. This is a player-coach role: you will manage, hire and develop engineers, and you will also stay close enough to the code to design agent architectures, review prompts and tool definitions, and debug a failing eval yourself.

You will own how our agents are built, measured and shipped: the skills and tools they call, how they remember context across a case, and the evals that tell us whether a change made them better or worse. In a regulated domain like KYC and KYB, an agent that is confidently wrong costs our customers real money and audit risk, so quality and observability matter as much as speed.

What you'll do

Lead the team

• Manage a team of 5-6 AI and backend engineers: weekly 1:1s, clear goals, honest feedback and career growth plans.

• Hire the next wave of AI engineers. You will own the interview loop for agent-engineering roles and raise the bar on it.

• Run planning and delivery for the AI roadmap with product, so the team ships on predictable cycles without burning out.

• Build a culture where engineers write evals before they claim a win, and where incidents lead to fixes, not blame.

Build the agents, hands on

• Design and review our compliance agents, from single document checks to multi-step investigations that hand off to human reviewers.

• Build the reusable skills, tools and memory our agents rely on to call registries and data vendors and carry context across a case.

• Own the eval stack: golden datasets built with compliance experts, regression tests in CI and production monitoring.

• Write and review code about 50% of the time, and choose models based on accuracy, cost and latency.

What we're looking for

People leadership

• 2+ years managing engineers directly, including hiring, performance reviews and at least one hard conversation you handled well.

• A track record of growing engineers: someone you managed got promoted or took on a bigger scope because of your support.

• Experience running planning and delivery for a team in a fast-moving startup or scale-up.

Hands-on agent engineering

• 6+ years as a software engineer, with strong backend skills in Python or TypeScript.

• You have built and shipped LLM agents to production, not just prototypes, and can explain what broke and how you fixed it.

• Practical experience with each of these: designing tool and function-calling interfaces; packaging reusable skills or prompt modules; implementing agent memory (short-term context, long-term stores, retrieval); and building evals that gate releases.

• Comfort with at least one major model API (Anthropic, OpenAI or similar) and the trade-offs between agent frameworks and plain code.

• A habit of measuring: you can show an eval you built, the metric it tracked and a decision it changed.

How you work

• Clear written communication. You can explain an agent failure to a compliance lead and to an engineer.

• Good judgment on when to build, when to buy and when to keep a human in the loop.

Nice to have

• Experience in fintech, regtech, fraud, identity verification or another regulated domain where AI decisions need an audit trail.

• Document AI experience: OCR, document classification or extraction across many languages and formats.

• Experience with LLM observability and tracing tools, and with cost and latency optimization at scale.

• Contributions to open-source agent, eval or retrieval projects, or public writing on the topic.