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Addepar is a global data and AI platform serving 1,500+ firms managing nearly $10 trillion in assets. The Applied AI team works embedded with strategic clients to identify where AI can transform their workflows, then builds those capabilities from prototype to production.
In this role, you'll own the full lifecycle of AI-powered agent systems: understanding client problems deeply, prototyping quickly, rigorously evaluating AI behavior in real-world contexts, and shipping to production. You'll design and implement LLM integrations (prompt engineering, function calling, structured outputs, RAG workflows), define evaluation criteria for ambiguous AI correctness, work directly with clients to validate behavior, and partner with core engineering teams to generalize solutions into product features. You'll also contribute to shared AI infrastructure and maintain production systems with attention to reliability, cost, and performance.
The team is new and growing. Your work directly shapes what ships. The role involves occasional work outside standard business hours to accommodate client time zones and up to 15% travel to client sites for multi-day engagements.
Requirements:
- Extensive professional software engineering experience
- Professional experience with Python (primary team 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)
- Demonstrated ability to build from zero to one; drawn to figuring out what to build, not just executing specifications
- Bias for action: ship quickly, learn from feedback, iterate
- Track record seeing projects through from prototype to production with focus on client experience
- BS/MS in Computer Science, Mathematics, or equivalent quantitative field, or equivalent professional experience
Nice to have:
- Experience building agentic systems (multi-step tool use, agent orchestration, durable workflows)
- Additional backend languages (Java valued in their environment)
- Familiarity with financial data (portfolios, transactions, securities, accounting)
- Cloud infrastructure experience (AWS) and container-based deployments
- Experience with AI evaluation frameworks or methodologies
