Top Tech Jobs & Startup Jobs

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, ensure scalability and reliability, monitor performance and data drift, manage incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and deliver measurable business value.

Supports governance and portfolio management across technology initiatives by tracking program health, risks, dependencies, milestones, and performance metrics. Develops dashboards, executive communications, and reports; facilitates alignment across engineering, product, operations, and business teams; identifies execution risks and process improvements; and supports portfolio planning, prioritization, and governance reviews.

Own payment resiliency solutions in EEMEA, serving as a subject matter expert and partnering with sales, account management, consulting, customer success, service, and engineering teams. Develop business cases, analyze market and customer data, identify product opportunities, improve performance and utilization, incorporate customer feedback, and track product risks, issues, and dependencies. The role requires strong stakeholder management, communication, analytical, leadership, and payment authorization expertise.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and workflows, manages model performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and deliver business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, defines success metrics with stakeholders, manages model performance and data drift, ensures governance and compliance, and leads incident response and root-cause analysis. The role bridges technical and business teams while driving cross-functional initiatives and operational excellence.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and CI/CD workflows, manages reliability and incidents, monitors model performance and data drift, and ensures compliance with governance and ethical AI requirements. Partners with AI engineers, product teams, risk stakeholders, and business leaders to define success metrics, translate business needs into technical requirements, and deliver measurable value.

Lead product development for Commerce Media advertiser solutions: define strategy, write product requirements, prioritize roadmaps, manage delivery with Engineering and TPMs, support GTM and business cases, drive ideation and research, mentor junior PMs, and liaise across Sales, Ops, Legal, Privacy, and other stakeholders to deliver new features and optimizations.

Supports Mastercard’s Multimedia Services and Employee Digital Experience teams through lifecycle refreshes, transformational initiatives, regional service delivery, and event support. The intern troubleshoots technical issues, coordinates multiple projects and tasks, adapts to new technologies, and contributes to global workplace technology initiatives. The role requires Python proficiency, audio/visual or broadcast technology experience, strong analytical skills, and effective communication.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, identify performance issues and data drift, manage incidents and root-cause analysis, and ensure reliable, scalable operations. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and connect AI deployments to business objectives.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and workflows, monitors model performance and data drift, resolves incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, and governance functions to define success metrics, meet compliance requirements, and maximize business value.