Top AI & Machine Learning Jobs

Lead design, roadmap, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Architect compute, GPU, storage, and networking for training and low‑latency inference at petabyte scale; run RFIs/RFPs; define data center and cooling requirements; partner with data science, MLOps, security, and governance; influence executives and evaluate emerging AI infrastructure technologies.

Lead design, procurement, and lifecycle management of on-prem AI infrastructure. Act as product owner translating data science needs into scalable compute, storage, networking, and accelerator platforms. Drive RFI/RFPs, architect high-performance systems for large-scale training and low-latency inference, define infrastructure standards, partner with MLOps/data science/security/governance, influence executives, and evaluate emerging AI infrastructure technologies.

Lead project and program management for Finance AI initiatives, establishing governance, intake/prioritization, delivery plans, executive reporting, risk and benefits tracking, stakeholder alignment, and value realization across cross-functional teams.

Lead product strategy and execution for an enterprise AI platform (Agent Factory) to enable design, build, evaluation, deployment, monitoring, governance, and commercialization of AI agents. Manage a small product team, partner with engineering, security, legal and business stakeholders, embed responsible AI and governance controls, drive adoption and metrics, and prioritize platform investments to scale trusted, production-ready agents.

Lead design and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain and vector search. Architect scalable, secure cloud/data systems (AWS, Cloudera, Databricks), build knowledge bases and APIs, optimize models in production, mentor engineers, and ensure data governance, security, and compliance.

Join the AI Innovation at Scale team to design and execute test strategies, perform white-box and black-box testing, validate data using Impala and Spark, monitor Spark job execution, collect logs, analyze quality data, and work with stakeholders to resolve issues and improve QA processes.

Lead design and delivery of enterprise-scale AI/ML solutions for Ethoca, building and governing models (NLP, generative, multi-modal) to reduce chargebacks, improve dispute resolution, and scale production systems across cloud environments.

Lead development, deployment, and monitoring of end-to-end ML/DL/AI solutions. Responsibilities include data extraction, cleaning, modeling, evaluation, production deployment, documentation, and presenting results. Collaborate across teams, build prototypes and POCs, protect IP, and disseminate knowledge externally.

Lead a small team and contribute hands-on to design, develop, deploy, and iterate AML and crypto risk detection machine learning models. Own technical roadmap, collaborate with Product and Engineering, ensure model quality, explainability, documentation, and governance, and drive production performance and outcomes across the model lifecycle.