Machine Learning Engineer (MLOps)

Location: Chicago, IL; New York, NY; Redwood City, CA. Chicago office hybrid schedule: 4 days in-office and 1 day remote.

Company

hirify.global operates Klover, a U.S. fintech platform providing financial tools, rewards, and services to more than one million monthly active users.

What you will do

• Build, deploy, and operate production ML systems supporting earned wage access and other B2C financial services.
• Develop reusable feature, training, serving, and model lifecycle pipelines for decisioning, fraud, churn, transaction intelligence, and consumer behavior use cases.
• Build ML infrastructure in GCP and Kubernetes using Terraform, CI/CD, and workflow automation.
• Implement model monitoring, alerting, dashboards, drift detection, and automated retraining with tools such as Prometheus and Grafana.
• Enable data scientists to deploy, iterate on, and retrain models safely and efficiently.
• Use AI coding agents to write, test, debug, and ship infrastructure and pipeline code, verifying outputs with sound engineering judgment.

Requirements

• 5+ years of experience building and operating production ML systems as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist, or similar.
• Strong Python, SQL, software engineering, and platform engineering skills.
• Experience with production model deployment, serving, monitoring, retraining, feature engineering, training/serving parity, versioning, reproducibility, and progressive rollouts.
• Hands-on experience with Docker, Kubernetes, Terraform, Airflow, CI/CD for ML, and cloud platforms; GCP is preferred.
• Experience with low-latency online model serving, distributed computing or GPU workloads, and large real-world datasets.
• Strong communication skills and the ability to explain technical topics to technical and non-technical audiences.

Nice to have

• STEM degree or related quantitative education.
• Experience with Go or Rust, service meshes such as Istio, Spark, Ray, or Dask.
• Experience with model explainability, auditability, and compliance in regulated credit or fintech decisioning.

Culture & Benefits

• Work on production-grade systems prioritizing reliability, security, privacy, performance, and cost efficiency.
• Collaborate with backend, frontend, data science, analytics, platform, product, and business teams.
• Take on responsibilities across engineering, infrastructure, and ML execution according to business needs.
• Contribute to financial products used by more than one million active users each month.