SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Attain (formerly Klover) is a fast-growing fintech platform serving over one million active users monthly, processing and moving more than $1.5 billion annually. The company provides real-time access to financial tools, rewards, and services designed to help people improve their financial lives.
You will own production ML systems and build out the MLOps platform infrastructure powering Attain's suite of B2C financial services. This is a highly hands-on, infrastructure-first role focused on designing, building, and operating the pipelines, platforms, and tooling that take models from experiment to reliable production service across the app portfolio—and keeping those systems healthy, performant, and cost-effective once live.
Your work will span the systems and infrastructure behind high-impact predictive models, including pipelines, feature infrastructure, model-serving, CI/CD, and observability that keep them reproducible, automated, monitored, and fast in production. Day to day, you'll build the platform and automation that enables the team to move fast without sacrificing performance—streamlining retraining and rollouts, tuning systems for speed and efficiency, and building the metrics and alerting that give confidence to ship. You'll enable data scientists to deploy and iterate on models quickly and safely.
Key responsibilities include: building, deploying, and operating production ML systems at the core of Attain's products with focus on reliability and performance; building and improving pipelines and serving infrastructure behind predictive models for consumer decisioning, fraud detection, churn prediction, and transaction intelligence; owning the production side of the model lifecycle including feature pipelines, deployment, CI/CD, monitoring, and automated retraining; building reusable modeling pipelines and feature engineering systems deployed via Terraform and CI/CD into GCP + Kubernetes; instrumenting models with monitoring, alerting, and automated retraining using tools like Prometheus/Grafana; directing AI coding agents as a force multiplier to write, test, and ship infrastructure code with strong judgment about verification; automating manual steps in the ML lifecycle; partnering with data scientists to provide fast, safe deployment paths; and collaborating with analysts, platform engineers, product managers, and business stakeholders.
The ideal candidate combines strong software and platform engineering fundamentals with practical MLOps experience building and operating production ML systems from scratch, and treats modern AI tooling as a first-class part of the work—directing coding agents to write and ship infrastructure code with judgment about when to verify their output.
