Top AI & Machine Learning Jobs

Lead the development of AI and machine learning solutions at Mastercard, defining strategy and mentoring teams while ensuring compliance and enhancing consumer experiences.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Lead ML engineering for Operational Intelligence: design and productionize multimodal transformer and agentic systems, build scalable RAG/Graph-RAG and LLMOps/MLOps pipelines, implement inference services and memory/state subsystems, apply traditional and deep learning methods, enforce Responsible AI, and drive research-to-production innovation.

Lead hands-on design and delivery of production-ready agentic AI applications and platform capabilities. Drive architecture, SDLC best practices, observability, security, and automation. Collaborate with Applied AI, Product, and Platform teams to move projects from prototype to governed production while mentoring engineers and improving developer productivity using AI-enabled tooling.

Design and build scalable, production-grade AI-enabled and agentic applications and developer platforms. Own architecture, coding, testing, CI/CD, observability, security, and operational readiness. Integrate AI tools and workflows to improve engineering productivity and take AI solutions from prototype to production.

Design and build scalable, production-grade AI-enabled and agentic applications and platforms. Implement architectures, CI/CD, testing, observability, and secure, reliable distributed systems while improving engineering productivity via AI-assisted workflows.

Embed with Services teams to write production code, unblock delivery, and deploy AI-first solutions (LLMs, agents, RAG). Provide surge engineering, clear architectural and delivery bottlenecks, coach teams on AI practices, and deliver reusable patterns and reference implementations that scale across the organization.

Embed into Services teams to write production code, remove technical bottlenecks, and deliver AI-first features. Design and deploy production-grade AI assistants, agents, and RAG solutions; coach teams on AI practices; build reusable patterns and enablement to scale AI adoption while ensuring governance, observability, and handoff to product teams.

Embed with Services teams to write production code, unblock delivery, and deploy AI-first solutions (LLMs, agents, RAG). Provide surge engineering capacity, clear architectural and delivery bottlenecks, coach teams, deliver reusable AI patterns, and align work with enterprise standards.

Embed into Services teams as a hands-on Principal engineer to write production code, unblock delivery, provide surge capacity, design and deploy production-grade AI solutions (assistants, agents, RAG), coach teams on AI-first practices, transfer knowledge, and create reusable AI patterns aligned to enterprise standards.