Staff Platform Engineer - ML Ops

Job summary

Leads the design and evolution of secure, scalable ML platform capabilities that move machine learning solutions from experimentation into production, supporting financial-crime detection, remediation analytics, automation, and decisioning. Provides technical direction and builds reusable AWS-based tools and workflows for training, model registry, deployment, monitoring, infrastructure automation, and auditable reproducibility, enabling Data Scientists and engineers to operate ML workloads safely at scale.

Qualifications

• Deep experience in platform, cloud, DevOps, or infrastructure engineering, including productionizing machine learning workloads.
• Experience designing or building ML platforms, MLOps capabilities, or reusable engineering frameworks.
• Strong software engineering fundamentals, particularly in Python and automation.
• Understanding of the ML lifecycle and ability to work effectively with Data Scientists.

Responsibilities

• Provide technical leadership for ML platform architecture, standards, and engineering practices.
• Own the path from data-science experimentation to production, including training pipelines, model registry, managed MLflow, experimentation environments, and reproducible promotion.
• Build reusable platform capabilities, frameworks, and tools for productionizing ML solutions.
• Develop AWS-based capabilities for batch inference, deployment, and monitoring across the ML lifecycle.
• Create reusable Infrastructure as Code, CI/CD, and automation patterns with security and compliance controls.
• Build reproducibility, lineage, and audit evidence through versioned data, models, and code, and replayable runs.
• Partner with Data Scientists to translate ML lifecycle needs into scalable platform capabilities.
• Improve developer and Data Scientist experience with self-service tools, libraries, APIs, and paved roads.

Skills

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Industry

Relocation