AI/ML Production SRE Lead | MLOps & Reliability

Mastercard

Dundrum

Hybrid

EUR 120,000 - 170,000

Full time

14 days+

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Job summary

Mastercard seeks a Lead Site Reliability Engineer (AI/ML) to drive deployment, operationalization, and continuous improvement of AI/ML solutions. You will translate AI models from development to production, ensuring scalability, reliability, and measurable business impact while collaborating with AI engineers, product teams, and business stakeholders to define success metrics and governance.

You will implement MLOps best practices, automate workflows, and address incidents with root‑cause

Qualifications

Bachelor's degree in Computer Science, Engineering, Data Science, Business, or related field.
Minimum 8+ years of AI/ML operations experience in production environments.
Solid understanding of the AI/ML lifecycle from data preparation to deployment and monitoring.
Experience with one of the cloud platforms and their AI/ML services.
Proficiency in scripting and containerization technologies.
Knowledge of CI/CD pipelines for machine learning models.
Experience with monitoring tools for AI/ML solutions.
Understanding of data governance, data quality, and data security principles relevant to AI/ML.
Strong communication and stakeholder management skills.

Responsibilities

Lead the end-to-end deployment and operationalization of AI/ML models and solutions, ensuring they are scalable and reliable.
Establish and maintain robust monitoring frameworks; identify performance bottlenecks and data drift and drive resolutions.
Collaborate with business stakeholders, AI engineers, and product teams to define success metrics and align deployments with business objectives.
Implement MLOps best practices, automation strategies, and efficient workflows across the deployment lifecycle.
Coordinate with risk, compliance, and governance teams to ensure regulatory and ethical AI compliance.
Lead incident response for AI deployments, perform root cause analysis, and implement preventive measures.