MLOps Engineer (Fintech)

Location: On-site in Chicago, United States

Base salary: $120,000–$180,000 USD per year, plus equity.

Company

hirify.global is a payment service provider founded in 2023 that uses stablecoins, AI-driven fraud prevention, instant settlement, multi-currency FX, and unified payouts for marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants.

What you will do

• Build and operate real-time inference infrastructure for fraud, risk, and payment models in the transaction authorization path.
• Own reproducible training, validation, deployment, and feature pipelines using transaction, device, and chargeback data.
• Implement model versioning, registries, shadow mode, champion/challenger testing, canary rollouts, and rollback mechanisms.
• Monitor latency, score distributions, feature drift, approval rates, fraud, and chargeback outcomes through the observability stack.
• Integrate and evaluate third-party fraud and risk models using parallel and shadow testing.
• Build data and scoring capabilities for merchant-facing risk products and configurable blocking rules.

Requirements

• 4+ years of experience in MLOps, ML infrastructure, or ML platform engineering, ideally in fintech, payments, or another regulated environment.
• Production experience running models with real latency and uptime requirements; real-time inference in a transaction or decisioning path is a plus.
• Strong Python and production engineering skills, with the ability to read and write TypeScript/Node.
• Hands-on experience with ML pipeline and serving tools such as MLflow, Kubeflow, SageMaker, Vertex, BentoML, Feast, or similar, plus judgment about when to use them.
• Infrastructure experience with containers, Kubernetes, CI/CD, and at least one major cloud provider.
• Data engineering experience building reliable feature pipelines over operational, warehouse, and streaming data, with an understanding of training/serving skew.
• Experience monitoring production models for drift and calibration and connecting model metrics to fraud loss and approval rates.
• Familiarity with fraud, risk, or credit modeling and explaining model behavior to non-ML stakeholders such as banks and auditors.
• Comfort using AI-native tooling such as Claude Code daily, or genuine excitement to start.
• A bias toward shipping working model pipelines quickly rather than overbuilding a perfect platform.

Culture & Benefits

• Work on real-money payment authorization systems where latency, reliability, and explainability are critical.
• Use AI as a force multiplier for pipeline development, evaluation, and model operations.
• Health and wellness benefits, a 401(k) savings plan, and flexible time off.
• Eligible employees receive an equity grant tied to the company’s long-term success.
• Join a fast-growing payment infrastructure company serving businesses worldwide.