M
Mastercard
Senior Machine Learning Ops (AI Engineering)
Skills
Google Cloud PlatformDockerDatabricksGitCybersecurityMachine LearningAIAWSKubernetesWorkdayTerraformAzure
1 month ago
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Important information
Contract type:
Permanent
Location:
Ireland
Work mode:
Onsite
Published on:
24 September 2026
What they need
- 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
- 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
- Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines- Working knowledge of monitoring and observability practices: logging, metrics, tracing, and how they apply differently to latency-sensitive versus batch AI 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
- 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
- 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
- Familiarity with containerization (Docker; Kubernetes exposure a plus), particularly for packaging and deploying model-serving workloads
- 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
- 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 MLOps-specific tooling and practices: experiment tracking, model registries, and safe model deployment/rollout patterns (e.g., MLflow or equivalent)
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