Senior AI Engineer

Role details

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior AI Engineer Role Overview The Senior AI Engineer is responsible for designing, developing, deploying, monitoring, and continuously improving AI-enabled solutions that transform how Technology Regulatory Execution (TREx) delivers regulatory, customer, compliance, and risk management activities. This role combines strong software engineering, machine learning, generative AI, and automation capabilities to build scalable solutions that improve operational efficiency, enhance stakeholder experiences, and support regulatory excellence. As part of Technology Regulatory Market Compliance (TRMC), the Senior AI Engineer will partner with business, risk, compliance, and technology teams to identify transformation opportunities and develop AI-powered products that modernize regulatory examinations, customer assurance activities, technology control oversight, regulatory readiness, and risk management processes. This is a hands-on engineering role responsible for the full AI solution lifecycle, including model development, prompt engineering, model evaluation, deployment automation, observability, performance monitoring, continuous improvement, and production support. The successful candidate will leverage machine learning, generative AI, workflow automation, and cloud-based AI services to create innovative solutions that deliver measurable business outcomes while meeting enterprise governance, security, and compliance requirements. Key Responsibilities AI Solution Design & Development • Design, build, test, deploy, and support AI and machine learning solutions that improve the efficiency, quality, scalability, and consistency of TREx operations. • Develop prototypes, proofs of concept, and production-ready AI applications addressing regulatory examinations, customer requests, risk assessments, governance processes, and compliance activities. • Design and implement generative AI solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), vector databases, knowledge repositories, and enterprise AI platforms. • Translate complex business requirements into scalable AI architectures and technical solutions. • Develop reusable AI services, automation frameworks, prompt libraries, workflow templates, and accelerators that can be leveraged across multiple regulatory and risk domains. Model Building, Evaluation & Optimization • Build, train, evaluate, fine-tune, and optimize machine learning and generative AI models using established engineering and data science practices. • Apply iterative model development techniques, including experimentation, testing, refinement, retraining, and continuous performance improvements. • Execute model evaluation processes using appropriate quality, accuracy, relevance, reliability, and business outcome metrics. • Implement prompt engineering, model tuning, and hyperparameter optimization techniques to improve solution effectiveness and efficiency. • Establish guardrails and validation mechanisms that promote responsible, secure, explainable, and compliant AI outcomes. AI Operations & MLOps • Design and manage MLOps practices that support the complete AI lifecycle, including automated testing, deployment pipelines, model versioning, monitoring, and governance. • Develop CI/CD processes for AI solutions and integrate AI capabilities into enterprise technology platforms and workflows. • Implement telemetry, logging, observability, and monitoring capabilities that provide visibility into model performance, system reliability, adoption, and operational health. • Monitor AI solutions for model drift, data drift, performance degradation, and operational risks, taking corrective action as needed. • Support production AI environments by troubleshooting issues, enhancing system performance, and ensuring platform stability. Cloud & Platform Engineering • Develop and deploy AI solutions across public and private cloud environments using enterprise-approved cloud-native services and platforms. • Collaborate with architecture, engineering, and platform teams to establish scalable AI design patterns, deployment standards, and reusable technical capabilities. • Ensure AI solutions meet enterprise expectations for security, resiliency, availability, scalability, and data protection. Automation & Continuous Improvement • Identify opportunities to automate evidence collection, control execution, risk monitoring, testing activities, reporting processes, and stakeholder engagement workflows. • Design human-in-the-loop and feedback-driven processes that continuously improve AI capabilities and business outcomes. • Evaluate emerging technologies, AI frameworks, and industry trends to identify opportunities for modernization and innovation. • Measure and communicate AI solution performance, operational improvements, productivity gains, risk reduction, and stakeholder value realization. Governance & Responsible AI • Ensure AI solutions comply with regulatory requirements, enterprise risk policies, security standards, and Responsible AI principles. • Implement model governance, explainability, auditability, validation, and documentation practices throughout the AI solution lifecycle. • Partner with compliance, legal, information security, and risk management teams to support sustainable and compliant AI adoption. • Promote AI engineering standards, best practices, and governance frameworks across the organization. All About You Required Qualificatio