Sr AI Engineer - Data

3 нед. назад

126k–214.9k USD / yearUSASeniorRemote

typescriptpostgresqlawslambdasqseventbridges3terraform

Build and improve AI-driven data-heavy features and infrastructure for financial products at .

Own data-heavy features from design through production, planning pragmatic solutions for rising traffic and data volume.
Improve data models, pipelines, migrations, and multi-tenant systems.
Work in our TypeScript monorepo with Hono, Drizzle/Postgres, and AWS infrastructure managed with SST/Pulumi across Lambda, SQS, EventBridge, and S3. Experience with Terraform, AWS CDK, or similar tools transfers well. You may also touch React 19 and React Router 7 when work crosses into the product.
Monitor production, investigate issues, and fix root causes.
Help the team make clear, practical technical decisions.

5+ years building, scaling, and running production software.
Strong backend engineering skills in a typed language, including experience integrating third-party and public APIs such as OpenAI or Anthropic Claude.
Experience with relational data modeling, migrations, and production data systems.
Sound judgment when working with sensitive data and high-impact changes.
Current, hands-on experience using AI coding tools and reviewing their output.
Clear written communication, product sense, and comfort owning work end to end.
Experience with TypeScript, Postgres, serverless AWS, or event-driven systems is useful; exact framework overlap is not required.
If you enjoy turning complex data problems into simple, dependable systems - and want to help define AI-native engineering - we'd love to hear from you.

Build the data foundation behind financial products used by credit unions and their members.
Coach builds tools that help credit unions support members at important moments in their financial lives. We turn complex credit information into clear, useful next steps. Because our products handle sensitive financial data, we move quickly without compromising safety or trust.
AI agents are part of our engineering workflow, not an experiment. They handle meaningful implementation work; engineers set direction, review the output, and own what ships. You will use our agents, improve them, and help build what comes next.