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Addepar is a global data and AI platform serving 1,500+ firms managing nearly $10 trillion in assets across 60 countries. The Applied AI team works embedded with strategic clients to identify where AI can transform their workflows, then builds those capabilities from prototype through production.
In this role, you will build agentic systems end-to-end: starting with deep problem understanding alongside clients and domain experts, moving quickly through prototyping and rigorous evaluation, and shipping to production. You'll design and implement LLM integrations (prompt engineering, function calling, structured outputs, retrieval-augmented workflows), define evaluation criteria for AI systems where correctness is domain-specific and ambiguous, and work directly with clients to validate behavior and iterate toward production quality. You'll partner with core engineering teams to generalize client-specific solutions into product features, contribute to shared AI infrastructure (agent frameworks, tool integrations, deployment pipelines), and monitor production AI systems for reliability, cost, and performance.
This is a customer-facing role where you sit close to the people using what you build—that feedback loop drives both product and skill growth. The team is new and your work shapes what ships. The 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.
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 something from zero to one; drawn to figuring out what to build, not just building what's specified
- 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 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 valuable in their environment)
- Familiarity with financial data (portfolios, transactions, securities, accounting)
- Exposure to cloud infrastructure (AWS) and container-based deployments
- Experience with evaluation frameworks or methodologies for measuring AI system quality
