Senior Machine Learning Ops - AI Engineering

Engg; Dublin; On-site; EUR 120,000 - 150,000; Full time

Job summary

Mastercard in Dublin seeks a Senior Machine Learning Ops Engineer to design and operate the pipelines, deployment workflows and production-readiness practices turning models into governed services.

You will own experiment tracking with MLflow or equivalent, manage model registries, implement drift monitoring, and orchestrate training and inference on Databricks, while embedding security and observability across real-time and batch workloads.

Qualifications

• Experience with MLOps tooling and practices: experiment tracking, model registries, and safe deployment patterns.
• Experience supporting AI/ML workloads: deployment pipelines, batch or streaming inference.
• Hands-on CI/CD experience for production AI/ML environments.
• Monitoring, observability, logging, metrics, and tracing for AI workloads.
• Security best practices in cloud and CI/CD: secrets, IAM, least-privilege access.

Responsibilities

• Design and operate ML pipelines for training, evaluation and deployment.
• Implement canary/shadow deployment and model-version gating to prevent breaking changes.
• Orchestrate training/inference on Databricks and maintain job workflows.
• Embed security and governance across pipelines and provide observability.
• Collaborate with engineering, data science, and security teams to improve reliability.

Job description

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 Machine Learning Ops - AI Engineering. Responsible for building and operating the pipelines, deployment workflows, and production-readiness practices that turn trained models into reliable, governed services.

AI Model Lifecycle & Deployment

Own experiment tracking and model registry practices: using MLflow (or equivalent) to manage model versioning and the staging/production/archived lifecycle. Implement drift and model-performance monitoring: detecting data drift, embedding/representation drift, and downstream task performance degradation. Implement safe model release and rollout mechanisms: including canary or shadow deployment patterns for new model versions, version-gated promotion criteria, and rollback procedures, so downstream consumers are never broken by an untested release. Orchestrate training and inference workloads on Databricks: configuring and maintaining Databricks Workflows/Jobs for recurring training cycles and on-demand inference/embedding generation. Monitoring & Governance Design and implement observability for AI/ML services: logging, metrics, and distributed tracing across both real-time and batch workloads, with SLIs/SLOs appropriate to each. Set up automated evaluation gates for offline metrics and model performance degradation. Track cost and resource utilization for compute-intensive workloads: particularly GPU-based training and inference, flagging inefficiencies or budget risk.

Pipeline & Infrastructure Development

Design and build CI/CD pipelines for AI and data workloads: supporting model training, evaluation, and deployment, and recommending which tools and patterns to use within the organization's existing supporting technology. Embed security best practices into every pipeline: secrets management, least-privilege access control, and secure configuration, integrating correctly with existing organizational identity and security standards rather than defining new ones. Onboard platform services onto centrally-owned infrastructure: such as API gateways and cross-environment data pipelines: meeting their existing security and integration requirements. Support incident response and post-incident improvement: contributing to troubleshooting production issues and helping drive follow-up actions after incidents.

All About You - Required skills and experience, in priority order

• Experience with MLOps-specific tooling and practices: experiment tracking, model registries, and safe model deployment/rollout patterns (e.g., MLflow or equivalent).
• Experience supporting AI/ML workloads specifically: model deployment pipelines, batch or streaming inference, and the operational differences between training and serving workloads.
• Strong, hands-on experience building and maintaining CI/CD pipelines in production environments, including the judgment to recommend appropriate tools and patterns rather than simply operating an existing pipeline.
• Working knowledge of monitoring and observability practices: logging, metrics, tracing, and how they apply differently to latency-sensitive versus batch AI workloads.
• Familiarity with security best practices in cloud and CI/CD environments: secrets management, IAM, least-privilege access: with the ability to implement these correctly within an existing security framework.
• Experience with Databricks or a similar unified data/AI platform: job orchestration, workflow scheduling, and integration with governed data pipelines.
• Strong plus if not already present.
• Hands-on experience with cloud platforms, particularly AWS, as a consumer of managed services rather than an infrastructure architect.
• Experience with Azure or GCP also valuable.
• Experience with infrastructure-as-code tools (e.g., Terraform) sufficient to provision and configure resources within an existing account/platform structure.
• Familiarity with containerization (Docker; Kubernetes exposure a plus), particularly for packaging and deploying model-serving workloads.
• Strong understanding of software delivery practices: version control, automated testing, and release discipline.
• Strong problem-solving skills and comfort owning technical design decisions, working effectively across engineering, data, and AI teams without requiring extensive oversight.

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.