Top AI & Machine Learning Jobs

Leads the design and evolution of Mastercard’s enterprise AI platform, managing AI engineers and defining technical roadmaps. Builds shared services for model serving, inference, feature stores, vector databases, prompt management, governance, and self-service development. Establishes MLOps and LLMOps standards while designing cloud-native infrastructure with Kubernetes, containers, microservices, and infrastructure as code. Partners with security, data, architecture, and product teams to deliver secure, scalable, observable, reliable, and compliant AI solutions across the enterprise.

Lead the design and operation of an enterprise AI platform supporting production AI and Generative AI solutions. Manage and mentor AI engineers, define platform roadmaps, build shared services for model serving and orchestration, establish MLOps and LLMOps standards, and ensure scalability, reliability, security, governance, and compliance. Partner with architecture, cloud, data, security, product, and data science teams to accelerate enterprise AI adoption.

Leads the design and operation of Mastercard’s enterprise AI platform, including model serving, MLOps, LLMOps, cloud-native infrastructure, governance, security, observability, and reliability. Manages and mentors AI engineers, defines the platform roadmap, partners with architecture, security, data, and product teams, and enables production deployment of AI and generative AI solutions at enterprise scale.

Lead the design, development, and operation of Mastercard’s enterprise AI platform. Manage and mentor AI engineers while defining platform roadmaps, MLOps and LLMOps standards, cloud-native infrastructure, model-serving services, governance, security, observability, reliability, and automation. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI and generative AI solutions at scale.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, registries, and developer tools. Manage and mentor AI engineers, define the platform roadmap, establish MLOps and LLMOps standards, and ensure secure, scalable, reliable cloud-native infrastructure. Partner with architecture, security, data, product, and science teams to productionize AI solutions while implementing governance, observability, compliance, automation, and operational excellence.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, establish MLOps and LLMOps standards, build cloud-native services for model serving and orchestration, and ensure scalability, reliability, security, governance, and cost efficiency. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI and generative AI solutions while driving platform strategy, operational excellence, and engineering best practices.

Deploy into Mastercard Services engineering teams to write production software, resolve technical bottlenecks, accelerate critical roadmaps, and establish missing capabilities. Build production-grade AI assistants, agents, and RAG workflows; integrate evaluation, observability, governance, and responsible AI practices. Coach teams on AI-first development, transfer knowledge, create reusable reference implementations, and partner with architecture and platform teams to scale effective engineering patterns.

Leads AI quality engineering for generative AI, LLM, RAG, and agentic systems. Designs evaluation frameworks, automated test suites, red-team exercises, responsible AI audits, performance benchmarks, monitoring, and drift detection. Establishes quality standards and CI/CD quality gates while partnering with engineering and product teams to identify risks and improve reliability, safety, accuracy, and enterprise readiness.

Lead architecture and delivery of production-grade generative AI systems: multi-agent orchestration, multimodal transformers, RAG/Graph-RAG, LLM fine-tuning and LLMOps, inference microservices, responsible AI, and continuous model improvement to drive enterprise-scale outcomes.

Lead design, procurement, and scaling of Mastercard's on‑premise AI infrastructure. Define standards for compute, storage, networking, cooling and data center design; run RFI/RFPs; partner with data science, MLOps, platform, security, and governance teams to support large‑scale training and low‑latency inference for generative and predictive AI workloads.