AI Solutions Engineer at Affirm

About the Team:

People Tech & Analytics (PTA) builds and owns the data, AI, and technology infrastructure for Affirm's People function. The team runs like a product engineering group embedded in HR. We own the full stack: data ingestion, transformation, dashboards, AI tools, and production applications deployed on Snowflake.

About the Role:

This is a hands-on engineering role. You will build, deploy, and maintain AI-powered systems that serve the People function and the broader employee base. The work is taking messy business problems (fragmented knowledge, manual processes, disconnected tools) and turning them into working software: agents, APIs, applications, and infrastructure. You will work closely with partners across the People function who own domain expertise and stakeholder relationships. Your job is to turn their rough applications and processes into production systems, and to push back when a technical constraint changes what's possible. This is not a pure backend role. You will be in the room when business problems are being scoped, and you need to understand the problem well enough to make architecture decisions on your own. You also need to take what you build and get it running in production. Hosting, security, deployment, and ongoing maintenance are all part of the job.

What You'll Do:

• Build and ship AI agents, APIs, and applications on Affirm's internal platform (Snowpark Container Services / Quicksilver). You own the full lifecycle: architecture, containerization, networking, secrets, CI/CD, monitoring, and fixing what breaks.

• Turn messy business requirements from People Operations stakeholders into production systems. Integrate with Workday, Notion, and case management tools so AI surfaces real answers from governed content, not model guesses.

• Navigate Affirm's existing security and data governance infrastructure to get AI systems running safely on people data. RBACs, data classification, and access policies already exist, but connecting them across systems (Workday, Snowflake, case tools) is where it gets messy. You figure out what's allowed, build within those constraints, and make sure employee data stays where it's supposed to.

• Design reliability infrastructure for multi-model LLM services. Structured output validation, fallback chains, circuit breakers for external APIs, and quality controls that catch hallucination before users see it.

• Work directly with non-technical stakeholders to scope problems, make architecture decisions, and give honest assessments of what AI can and can't do. You translate in both directions.

• Contribute to the team's shared Python codebase, dbt models, and Snowflake infrastructure as part of a small, full-stack team that ships fast.

• Own what you build. When something breaks in production, you diagnose and fix it.

What We're Looking For:

• Software engineering foundation. You have built, deployed, and maintained production applications. You understand version control (Git/GitHub), CI/CD pipelines, containerization, and what it takes to keep software running, not just written.

• Systems thinking and technical architecture. You understand how software systems fit together: databases, APIs, authentication, hosting, deployment pipelines. You can make architecture decisions, evaluate trade-offs, and read code well enough to know when something is wrong. The team works primarily in Python, and you should be comfortable in it, but the ability to think in systems matters more than raw coding skill.

• Builder disposition. You have created something from nothing: a system, a tool, a platform, in an environment where nobody handed you a spec. You identified the problem, designed the solution, and shipped it.

• Ability to work across the technical-business boundary. You can sit in a meeting with non-technical stakeholders, understand the real problem behind the stated request, and come back with a solution that actually addresses it. You translate in both directions: technical const

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