About this role
Data Management Maintain secure, reliable data pipelines for model training and inference. Ensure data quality checks (validity, completeness, freshness) before retraining. Track data lineage and provenance to support audits and compliance. Apply data governance frameworks across multi-cloud environments. Bachelor's degree in computer science, Information Technology, or a related field. Professional certifications in relevant technologies or infrastructure management are preferred. Typically, 5-10 years of related infrastructure experience required; experience in the securities or financial services industry is a plus. Provide documentation, runbooks, and knowledge bases for model operations. Collaborate with Data Science, DevOps, and Compliance teams. Educate stakeholders on model behaviors, risks, and limitations. Conduct postmortems for model failures or degraded performance. Continuous Improvement Benchmark models and platforms across Azure, Google Cloud, and hybrid environments. Incorporate new MLOps/ModelOps tooling for efficiency and compliance. Establish feedback loops from business outcomes back into model evaluation. Regularly reassess KPIs and SLOs to align with evolving business needs. Here's a few of our recent awards: America's Most Innovative Companies, Fortune, 2025 World's Most Admired Companies, Fortune 2025 “Most Just Companies”, Just Capital and CNBC, 2025 Document models for auditability and transparency. Enforce responsible AI principles (fairness, explainability, bias mitigation). Ensure compliance with regulations (GDPR, HIPAA, SOC 2, industry-specific rules). Maintain approval workflows for promoting models into production. Security & Access Control Control access to model APIs and training datasets (least-privilege IAM). Protect sensitive data with encryption at rest and in transit. Monitor and prevent adversarial attacks or misuse of AI models. Conduct regular security reviews of deployed models and APIs. Reliability & Scalability Implement autoscaling of inference services based on demand. Design for high availability and disaster recovery across regions/clouds. Perform load testing for AI services under peak conditions. Use A/B testing and canary releases for safe rollouts of new model versions. Automation & Optimization Automate retraining pipelines based on triggers (new data, performance thresholds). Optimize infrastructure usage (e.g., GPU/TPU scheduling, spot instances). Apply FinOps practices to control costs of training and inference. Leverage AI Ops for predictive maintenance of AI services.
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About BNY
For more than 240 years BNY has partnered alongside clients, using its expertise and platforms to help them operate more efficiently and accelerate growth. Today BNY serves over 90% of Fortune 100 companies and nearly all the top 100 banks globally. BNY supports governments in funding local projects and works with over 90% of the top 100 pension plans to safeguard investments for millions of individuals. As of December 31, 2025, BNY oversees $59.3 trillion in assets under custody and/or administration and $2.2 trillion in assets under management.
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What you'll do
- Maintain secure and reliable data pipelines for model training and inference while ensuring data quality and compliance. Collaborate with various teams and conduct postmortems for model failures to drive continuous improvement.
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