Public Cloud Operations, Senior Vice President, Production Services Infrast at BNY

Lake Mary, Florida, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

0.0

Posted On

22 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Management, Data Quality Checks, Data Governance, Documentation, Collaboration, Continuous Improvement, Model Auditability, Responsible AI Principles, Security, Access Control, Reliability, Scalability, Automation, Optimization, MLOps, Model Operations

Industry

Financial Services

Description

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 or a related discipline, or equivalent work experience required; advanced degree preferred. 10 -12 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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Responsibilities

Maintain secure and reliable data pipelines for model training and inference while ensuring data quality and compliance. Collaborate with various teams to educate stakeholders on model behaviors and conduct postmortems for model failures.