Senior AI Engineer

About the Role

The Senior AI Engineer is the technical force multiplier behind ClassWallet’s AI transformation. Partnering directly with the Sr. Director, AI & Automation, you will design, build, and ship the AI agents, automations, and internal tools that accelerate how every team in the company gets work done — and you will selectively contribute AI capabilities into our customer-facing products.

This is a hands-on, builder-first role. You will spend your days writing code, prototyping agents, embedding with business teams to understand their workflows, and turning ambiguous problems into shipped solutions that produce measurable results in days and weeks — not quarters. You’ll set the technical bar for how AI is built and operated at ClassWallet.

If you light up at the idea of giving every employee a meaningful AI capability, watching teams transform their throughput, and occasionally putting that same craft into a product millions of dollars in public funds flow through, this is your role.

What You’ll Do

Build and Ship

Design, prototype, and deploy AI agents, LLM-powered workflows, and automations that measurably accelerate internal work across operations, finance, support, compliance, sales, and engineering
Build production-grade integrations between AI systems and ClassWallet’s stack — CRM, support, finance, ops, and engineering tools
Develop reusable components, prompt libraries, evaluation frameworks, and internal platforms so AI adoption scales across the company
Contribute AI capabilities to customer-facing product features in partnership with Product & Engineering
Embed and Translate

Sit alongside business teams to understand their workflows, identify high-leverage AI opportunities, and ship solutions to those teams directly
Pair with non-technical employees to build, refine, and operationalize their own AI workflows
Translate ambiguous business problems into concrete, shippable systems
Set the Technical Bar

Make pragmatic choices across the AI stack: LLMs, RAG, agent frameworks, RPA, low-code automation, and traditional software engineering — picking the right tool for the job
Establish standards for AI development at ClassWallet: code quality, evaluation, monitoring, prompt management, and cost control
Stay ahead of a fast-moving field and bring relevant innovations into the company before they’re obvious
Operate Responsibly

Partner with the Sr. Director, AI & Automation and Legal/Compliance on responsible AI practices, data privacy, and regulatory alignment appropriate to fintech and public-sector data
Build evaluation and observability into AI systems so we know they are working, improving, and behaving as intended

Requirements
6+ years of professional software engineering experience, with 2+ years building AI/ML or LLM-based systems in production
Strong proficiency in Python and modern AI development tooling — OpenAI, Anthropic Claude, LangChain/LangGraph, LlamaIndex, or comparable agent and RAG frameworks
Demonstrated experience designing and shipping AI agents and LLM workflows that solved real business problems with measurable outcomes
Working fluency with automation platforms (Zapier, n8n, Make, UiPath, or similar) and the judgment to choose between code and low-code for the job at hand
Comfort with APIs, webhooks, and integrating across SaaS systems (Salesforce, HubSpot, Jira, Slack, Google Workspace, etc.)
Pragmatic engineering instincts — bias to ship, comfort with ambiguity, ability to deliver value quickly
Strong communication skills; able to move fluently between executive stakeholders, business users, and engineers
Based in the US and able to work effectively in a remote or hybrid setup, with periodic travel to HQ as needed
Preferred

Experience in fintech, govtech, or other regulated industries
Familiarity with vector databases, embeddings, evaluation frameworks (e.g., Ragas, OpenAI Evals), and AI observability tooling (e.g., LangSmith, Helicone, Langfuse)
Experience building internal AI platforms or developer tools that scaled across an organization
Prior work shipping customer-facing AI features in a SaaS product
Background in product engineering, data engineering, or applied ML
A track record of coaching non-technical colleagues into productive AI users