Top AI & Machine Learning Jobs

Leads the design and evolution of an enterprise AI platform supporting experimentation through production. Manages and mentors AI engineers, defines the technical roadmap, and develops shared capabilities such as model serving, feature stores, vector databases, AI gateways, and registries. Establishes MLOps and LLMOps standards, cloud-native infrastructure, governance, security, observability, reliability, and automation practices. Partners with architecture, cloud, data, security, product, and data science teams to scale secure, resilient, and cost-effective AI adoption.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, define platform roadmaps, establish MLOps and LLMOps standards, and build services for model serving, inference, feature stores, vector databases, governance, and observability. Design secure cloud-native infrastructure using Kubernetes and infrastructure as code, ensure reliability and scalability, productionize AI solutions, and collaborate with architecture, security, data, product, and science teams.

Lead the design and evolution of Mastercard’s enterprise AI platform, managing and mentoring AI engineers while defining platform roadmaps, MLOps and LLMOps standards, cloud-native infrastructure, model serving, governance, security, observability, and reliability practices. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI solutions and deliver scalable, secure, reusable capabilities that accelerate enterprise AI adoption.

Lead the design and evolution of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, MLOps, LLMOps, governance, and developer self-service capabilities. Manage and mentor AI engineers while defining the technical roadmap and engineering standards. Build secure, scalable cloud-native infrastructure using Kubernetes, containers, microservices, and infrastructure as code. Partner with architecture, security, data, product, and data science teams to productionize AI solutions and ensure reliability, compliance, observability, and operational excellence.

Lead the design and evolution of an enterprise AI platform supporting AI and generative AI development, deployment, and operations at scale. Manage and mentor AI engineers, define the technical roadmap, establish MLOps and LLMOps standards, and build cloud-native platform services including model serving, vector databases, and AI gateways. Drive security, governance, reliability, observability, scalability, disaster recovery, and cost optimization while partnering with architecture, cloud, data, security, product, and data science teams.

Leads the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, feature stores, vector databases, governance, observability, and developer self-service tools. Manages and mentors AI engineers, defines the platform roadmap, establishes MLOps and LLMOps standards, and partners with architecture, cloud, security, data, and product teams. The role drives scalable, secure, reliable productionization of AI and generative AI solutions using cloud-native infrastructure and engineering best practices.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, MLOps, LLMOps, governance, and developer tooling. Manage and mentor AI engineers while defining the technical roadmap and platform standards. Build secure, scalable cloud-native infrastructure using Kubernetes, containers, microservices, and infrastructure as code. Partner with data science, security, architecture, and product teams to productionize AI solutions and ensure reliability, compliance, observability, and operational excellence.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, feature stores, governance, observability, and self-service capabilities. Manage and mentor AI engineers while defining platform strategy and technical roadmaps. Establish MLOps and LLMOps standards, build cloud-native infrastructure, and ensure scalability, reliability, security, compliance, and cost efficiency. Partner with data scientists, security, architecture, and product teams to productionize AI and generative AI solutions across the enterprise.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, feature stores, vector databases, governance, observability, and developer self-service. Manage and mentor AI engineers, establish MLOps and LLMOps standards, drive cloud-native infrastructure and reliability, and partner with architecture, security, data, product, and data science teams to productionize scalable, secure AI and Generative AI solutions.

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 such as model serving and vector databases, establish MLOps and LLMOps standards, and ensure security, governance, reliability, scalability, and cost efficiency across cloud-native infrastructure. Partner with architecture, security, data, product, and data science teams to accelerate enterprise AI adoption.