Addepar · Posted 13d ago
Software Engineer (Customer Facing Applied AI)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇬🇧 London, United Kingdom
- Contract
- Full-time
- Role
- Data science
- Source
- Stapply Data
Stack
- LLM
- SQL
- Python
- Java
- AWS
The posting
Join Addepar as a Software Engineer specializing in Customer Facing Applied AI. In this role, you will build AI-powered agents and tools from idea to production, working closely with clients to understand their workflows and validate AI behavior. You will design and implement LLM integrations, define evaluation criteria, and contribute to shared AI infrastructure. This is a unique opportunity to shape the future of AI in finance and be part of a dynamic team.
- Construire des systèmes agentiques de l'idée à la production, en comprenant bien le problème et en itérant rapidement. - Concevoir et mettre en œuvre des intégrations LLM, y compris l'ingénierie des invites, l'appel de fonction et les flux de travail augmentés par la récupération. - Travailler directement avec les clients pour comprendre leurs flux de travail, valider le comportement de l'IA et itérer vers une qualité de production.
- BS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience - Professional experience with Python (the team's primary language) - Prior professional software engineering experience - Comfort with SQL for data exploration and building data-grounded AI workflows - Experience with AI-assisted development tools (Claude Code, Codex, or similar) - Experience working with LLM APIs (Claude, GPT, or similar) including function calling and structured outputs - 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 does - You've built something from zero to one. You're drawn to figuring out what to build, not just building what's specified - Experience with evaluation frameworks or methodologies for measuring AI system quality - Exposure to cloud infrastructure (AWS) and container-based deployments - Familiarity with financial data (portfolios, transactions, securities, accounting) - Experience with additional backend languages (Java is valuable in our environment) - Experience building agentic systems (multi-step tool use, agent orchestration, durable workflows)