Leads hands-on MLOps initiatives by building production model deployment pipelines, Databricks platforms, infrastructure automation, feature engineering workflows, and model monitoring systems. Develops scalable training and inference pipelines, automates CI/CD-based model lifecycle processes, and ensures operational reliability, governance, security, and cost efficiency. Partners with data science, platform, and product teams to deliver AI solutions, establishes engineering standards, and mentors junior engineers.
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
Lead AI Engineer
Overview
AI Solutions, part of Mastercard's AI & Data organization, scales AI across the enterprise, moving use cases beyond pilots into trusted, production-grade capabilities embedded in Mastercard's platforms and products. Centralizing this capability drives speed to scale, operational resilience, consistent delivery standards, and responsible AI by design, in close partnership with the AI Center of Excellence.
This position sits on the Horizontal Enablement team, reporting to the Manager, AI Engineering, and is a senior hands-on contributor to the team's MLOps initiatives. Horizontal Enablement bridges platform teams and data science by setting engineering and data science operating standards for production models and maintaining domain-specific feature and model monitoring. As a Lead AI Engineer, you will build and operate the model deployment pipeline, the domain model monitoring platform, and the Databricks and infrastructure automation that allow AI and machine learning systems to run reliably at scale, while mentoring engineers across the team.
Role
As a Lead AI Engineer, you will:• Design, develop, and maintain MLOps capabilities and advanced AI and machine learning systems that address specific business challenges.• Implement models into production, building scalable training pipelines and deployment frameworks that handle large data volumes and high request rates.• Administer and maintain Databricks workspaces, including provisioning and configuration, cluster and compute policies, job orchestration, runtime and library upgrades, catalog and access management, secrets, monitoring, and cost optimization.• Deploy and maintain AI and machine learning infrastructure through infrastructure as code and automated release pipelines, keeping environments repeatable, secure, auditable, and consistent.• Build and optimize data ingestion, preprocessing, and feature engineering workflows that support model training and inference.• Automate model training, testing, deployment, and update workflows following CI/CD best practices.• Build and maintain domain-specific feature and model monitoring, tracking performance metrics and drift and updating models to sustain high-quality outputs.• Implement onboarding and operating standards for platform users, including naming and packaging conventions, validation rules, and exception handling.• Ensure the operational stability and scalability of AI systems, adhering to ethical guidelines and contributing to the organization's AI infrastructure.• Influence stakeholders and partner with data science, platform, and product teams to translate requirements into technical solutions.• Guide and mentor junior engineers through on-the-job experiences and code and design reviews, fostering continuous improvement across the discipline.
Required Qualifications• Master's degree with 3+ years of relevant experience, or Bachelor's degree with 5+ years, in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; equivalent practical experience considered.• Hands-on MLOps experience across model monitoring, feature catalogs, experiment tracking, model registry, and lifecycle CI/CD pipelines.• Hands-on experience administering Databricks workspaces, including cluster and compute policies, job orchestration, runtime and library upgrades, permissions, and secrets.• Experience deploying and maintaining infrastructure through infrastructure as code and automated pipelines, including environment provisioning, configuration management, and controlled release and rollback.• Strong experience with Spark and distributed data processing.• Proficiency in Python, PySpark, and SQL.• Hands-on experience with CI/CD and build tooling such as Git, Jenkins, Maven, and Artifactory.• Experience building and optimizing feature engineering and large-scale data processing workflows.• Strong understanding of machine learning and deep learning techniques, model lifecycle management, and production AI systems.• Experience with model deployment, evaluation, observability, optimization, and operational support.• Experience with cloud operations across public and private cloud environments.• Ability to communicate technical concepts clearly, work independently, and mentor other engineers.
Preferred Qualifications
• Experience with MLOps tools such as MLflow, Comet, or Weights and Biases.• Experience automating Databricks administration and deployment with the Databricks CLI, REST APIs, Asset Bundles, or the Databricks Terraform provider.• Experience with infrastructure as code and configuration tooling such as Terraform or Ansible, and with Docker and Kubernetes.• Experience contributing to engineering standards, governance frameworks, or observability practices for production AI systems.• Experience with Generative AI, LLMs, RAG, or agentic AI applications.• Familiarity with AI-assisted development tools such as GitHub Copilot or Claude Code.• Experience with data governance tooling, including data catalogs, lineage, role-based access control, and sensitive data handling.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:• Abide by Mastercard's security policies and practices;• Ensure the confidentiality and integrity of the information being accessed;• Report any suspected information security violation or breach, and• Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
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
Lead AI Engineer
Overview
AI Solutions, part of Mastercard's AI & Data organization, scales AI across the enterprise, moving use cases beyond pilots into trusted, production-grade capabilities embedded in Mastercard's platforms and products. Centralizing this capability drives speed to scale, operational resilience, consistent delivery standards, and responsible AI by design, in close partnership with the AI Center of Excellence.
This position sits on the Horizontal Enablement team, reporting to the Manager, AI Engineering, and is a senior hands-on contributor to the team's MLOps initiatives. Horizontal Enablement bridges platform teams and data science by setting engineering and data science operating standards for production models and maintaining domain-specific feature and model monitoring. As a Lead AI Engineer, you will build and operate the model deployment pipeline, the domain model monitoring platform, and the Databricks and infrastructure automation that allow AI and machine learning systems to run reliably at scale, while mentoring engineers across the team.
Role
As a Lead AI Engineer, you will:• Design, develop, and maintain MLOps capabilities and advanced AI and machine learning systems that address specific business challenges.• Implement models into production, building scalable training pipelines and deployment frameworks that handle large data volumes and high request rates.• Administer and maintain Databricks workspaces, including provisioning and configuration, cluster and compute policies, job orchestration, runtime and library upgrades, catalog and access management, secrets, monitoring, and cost optimization.• Deploy and maintain AI and machine learning infrastructure through infrastructure as code and automated release pipelines, keeping environments repeatable, secure, auditable, and consistent.• Build and optimize data ingestion, preprocessing, and feature engineering workflows that support model training and inference.• Automate model training, testing, deployment, and update workflows following CI/CD best practices.• Build and maintain domain-specific feature and model monitoring, tracking performance metrics and drift and updating models to sustain high-quality outputs.• Implement onboarding and operating standards for platform users, including naming and packaging conventions, validation rules, and exception handling.• Ensure the operational stability and scalability of AI systems, adhering to ethical guidelines and contributing to the organization's AI infrastructure.• Influence stakeholders and partner with data science, platform, and product teams to translate requirements into technical solutions.• Guide and mentor junior engineers through on-the-job experiences and code and design reviews, fostering continuous improvement across the discipline.
Required Qualifications• Master's degree with 3+ years of relevant experience, or Bachelor's degree with 5+ years, in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; equivalent practical experience considered.• Hands-on MLOps experience across model monitoring, feature catalogs, experiment tracking, model registry, and lifecycle CI/CD pipelines.• Hands-on experience administering Databricks workspaces, including cluster and compute policies, job orchestration, runtime and library upgrades, permissions, and secrets.• Experience deploying and maintaining infrastructure through infrastructure as code and automated pipelines, including environment provisioning, configuration management, and controlled release and rollback.• Strong experience with Spark and distributed data processing.• Proficiency in Python, PySpark, and SQL.• Hands-on experience with CI/CD and build tooling such as Git, Jenkins, Maven, and Artifactory.• Experience building and optimizing feature engineering and large-scale data processing workflows.• Strong understanding of machine learning and deep learning techniques, model lifecycle management, and production AI systems.• Experience with model deployment, evaluation, observability, optimization, and operational support.• Experience with cloud operations across public and private cloud environments.• Ability to communicate technical concepts clearly, work independently, and mentor other engineers.
Preferred Qualifications
• Experience with MLOps tools such as MLflow, Comet, or Weights and Biases.• Experience automating Databricks administration and deployment with the Databricks CLI, REST APIs, Asset Bundles, or the Databricks Terraform provider.• Experience with infrastructure as code and configuration tooling such as Terraform or Ansible, and with Docker and Kubernetes.• Experience contributing to engineering standards, governance frameworks, or observability practices for production AI systems.• Experience with Generative AI, LLMs, RAG, or agentic AI applications.• Familiarity with AI-assisted development tools such as GitHub Copilot or Claude Code.• Experience with data governance tooling, including data catalogs, lineage, role-based access control, and sensitive data handling.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:• Abide by Mastercard's security policies and practices;• Ensure the confidentiality and integrity of the information being accessed;• Report any suspected information security violation or breach, and• Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard's security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
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