Senior Machine Learning Ops - AI Engineering
Mastercard1 day ago
Dublin, IrelandSenior
Responsibilities
- Own experiment tracking and model registry practices using MLflow or an equivalent tool.
- Implement model drift, representation drift, and downstream performance monitoring.
- Design safe model release workflows using canary or shadow deployments, promotion gates, and rollback procedures.
- Configure and maintain Databricks Workflows and Jobs for recurring training and on-demand inference or embedding generation.
- Build observability with logging, metrics, and distributed tracing for real-time and batch AI/ML services.
- Create automated evaluation gates and monitor compute cost and resource utilization, including GPU workloads.
- Design and maintain CI/CD pipelines for AI and data workload training, evaluation, and deployment.
- Implement secrets management, least-privilege access, and secure configuration within existing security frameworks.
- Onboard platform services to centrally managed infrastructure and support production incident response and follow-up improvements.
Requirements
- Hands-on experience with MLOps tooling and practices, including experiment tracking, model registries, and safe deployment patterns.
- Experience operating AI/ML workloads, including model deployment pipelines and batch or streaming inference.
- Strong production experience building and maintaining CI/CD pipelines and selecting appropriate tools and patterns.
- Working knowledge of logging, metrics, tracing, and observability for latency-sensitive and batch AI workloads.
- Familiarity with cloud and CI/CD security practices, including secrets management, IAM, and least-privilege access.
- Experience with Databricks or a similar unified data and AI platform; Databricks experience is strongly preferred.
- Hands-on experience with AWS managed services; Azure or GCP experience is also valuable.
- Experience with infrastructure-as-code tools such as Terraform.
- Familiarity with Docker and preferably Kubernetes for packaging and deploying model-serving workloads.
- Understanding of version control, automated testing, software delivery, and release discipline.
- Strong problem-solving skills and the ability to own technical design decisions across engineering, data, and AI teams.
Tech Stack
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).