Software Engineer - Customer Facing Applied AI

Who We Are Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,500 firms in 60 countries use Addepar to manage and advise on nearly $10 trillion in assets.

Its open platform integrates with 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and levels of complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, São Paulo, London, Edinburgh, Geneva, Warsaw, Dubai, Pune and Singapore.

The Role The Applied AI team works embedded with Addepar's strategic clients to figure out where AI can change how they work and then build those capabilities from scratch. You'll spend time understanding a client's world, identify what's worth building, prototype it, prove it works, and then partner with core engineering teams to ship it as product for all customers.

As an Applied AI Engineer, you'll build agentic systems from idea through production. The work starts with understanding the problem well enough to know what to build, then moves fast through prototyping and iteration. You'll design agent behavior, wire up tool integrations, tune evaluations until the AI actually works in practice. You'll sit close to the people who use what you build, and that feedback loop is how both the product and your skills grow fast.

The team is new. Your work shapes what we ship.

This role may require occasional work outside standard business hours to accommodate client time zones and up to 15% travel to client sites, including occasional overnight and multi-day engagements.

What You'll DoBuild AI-powered agents and tools end-to-end: identify the problem with clients and domain experts, prototype quickly, evaluate rigorously, and ship to production

Design and implement LLM integrations including prompt engineering, function calling, structured outputs, and retrieval-augmented workflows

Define evaluation criteria and build test harnesses for AI systems where correctness is ambiguous and domain-specific

Work directly with clients to understand their workflows, validate AI behavior, and iterate toward production quality

Partner with core engineering teams to generalize client-specific work into product features

Contribute to shared AI infrastructure (agent frameworks, tool integrations, deployment pipelines)
Monitor and maintain production AI systems with attention to reliability, cost, and performance

Who You Are Extensive professional software engineering experience

Professional experience with Python (the team's primary language)
Experience working with LLM APIs (Claude, GPT, or similar) including function calling and structured outputs

Comfort with SQL for data exploration and building data-grounded AI workflows

Experience with AI-assisted development tools (Claude Code, Codex, or similar)
You've built something from zero to one. You're drawn to figuring out what to build, not just building what's specified.

A bias for action. You ship quickly, learn from feedback, and iterate.

You see projects through from prototype to production and care about what the client experiences, not what the code doesBS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience

Nice to Have Experience building agentic systems (multi-step tool use, agent orchestration, durable workflows)

Experience with additional backend languages (Java is valuable in our environment)
Familiarity with financial data (portfolios, transactions, securities, accounting)
Exposure to cloud…