Senior Machine Learning Ops - AI Engineering

Mastercard; Malahide; On-site; EUR 110,000 - 150,000; Full time

Job summary

Mastercard in Ireland is seeking a Senior Machine Learning Ops - AI Engineering to design and operate pipelines, deployment workflows, and production-readiness practices turning trained models into reliable services. You will own experiment tracking with MLflow, implement model monitoring, orchestrate Databricks workloads, and embed security across CI/CD.

This role requires hands-on cloud experience (AWS/Azure/GCP), Databricks, Terraform, Docker, and Kubernetes, plus a collaborative,

Qualifications

• Experience with MLOps tooling: experiment tracking, model registries, and safe deployment/rollout.
• Experience supporting AI/ML workloads: deployment pipelines, batch or streaming inference.
• Hands-on experience building CI/CD pipelines in production environments.
• Familiarity with monitoring and observability practices for AI workloads.
• Security best practices in cloud and CI/CD environments, IAM and least privilege.
• Experience with Databricks or a similar unified data/AI platform.
• Hands-on cloud experience (AWS/Azure/GCP) as a consumer of managed services.
• Infrastructure-as-code tools (Terraform) to provision resources.
• Containerization with Docker; Kubernetes exposure is a plus.
• Software delivery practices: version control, automated testing, release discipline.

Responsibilities

• Own experiment tracking and model registry practices for model versioning and lifecycle.
• Implement drift and model-performance monitoring for AI models.
• Implement safe model release and rollout mechanisms with canary/shadow patterns.
• Orchestrate training and inference workloads on Databricks with workflows.
• Design observability: logging, metrics, tracing for real-time and batch workloads.
• Set up automated evaluation gates for offline metrics and model degradation.
• Design and build CI/CD pipelines for AI/data workloads and security integration.
• Embed security into pipelines: secrets management and least-privilege access.
• Onboard platform services onto central infrastructure and support incident response.

Skills

MLflow

Model deployment

CI/CD pipelines

Databricks

Docker

Kubernetes

AWS

Azure

GCP

Terraform

Tools

Databricks

MLflow

Terraform

Docker

Kubernetes

AWS

Azure

GCP

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.