Senior Machine Learning Operations Engineer

TL;DR

Senior Machine Learning Operations Engineer (MLOps): Build and operate real-time inference and the production ML lifecycle for risk decisioning models with an accent on low-latency, high-availability serving, deployment safety (shadow/canary/champion-challenger), and granular observability. Focus on end-to-end model registry-to-retraining operations, including drift detection and experimentation routing.

Company

hirify.global is a fintech company using machine learning for risk decisioning and fraud/financial crime outcomes.

What you will do

• Build and operate the real-time inference service for the risk decision engine with low latency and high availability.
• Own model deployment infrastructure including registry/versioning, CI/CD with performance/bias/consistency checks, shadow mode, and staged rollouts.
• Implement model observability: availability, latency, error monitoring, and drift detection as a retraining trigger.
• Partner with Risk Data Science to run models from development-to-production handoff through production operations under MLP ownership.
• Deliver experimentation and routing capabilities such as champion/challenger and canary routing, plus explainability outputs like SHAP attributions.
• Contribute to a new platform team with strong product ownership across small and medium projects.

Requirements

• Location: Work from San Francisco, CA; New York, NY; Portland, OR; or Remote within Canada or the United States.
• 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field.
• Production ML service experience deploying, serving, and operating models in low-latency, high-availability contexts.
• Strong backend fundamentals in Python with API frameworks such as FastAPI or Flask.
• Experience with model deployment and lifecycle tooling: model registries, CI/CD for models, versioning, and staged rollout patterns (shadow, canary, champion/challenger).
• Experience building production observability and alerting (latency, errors, and ideally drift) plus comfort with SQL, low-latency stores (e.g., Redis/DynamoDB), and streaming pipelines (e.g., Kafka/Kinesis/Redpanda).

Culture & Benefits

• Total rewards include base salary, equity, and benefits.
• Salary and equity ranges are competitive for the SaaS/fintech industry and updated regularly.
• New hire offers are based on experience, expertise, geographic location, and internal pay equity.
• Equal Employment Opportunity employer with accommodations available during the recruitment process.

Hiring process

• Interviews and evaluations focused on production ML operations, backend fundamentals, and platform ownership.
• Compensation discussion based on experience, expertise, geographic location, and internal pay equity.

Salary: $166,600 - $208,300 (US employees, any location)