ML OPS Engineer

Job description

Description and Requirements The Team You Will Join When you join MetLife’s Global Technology team, you’ll be part of a forward-thinking group dedicated to shaping the future of digital solutions for customers worldwide. You’ll develop, maintain and support technology applications and delivery, leveraging AI, automation, and contemporary ways of working to enhance experiences and drive business outcomes. Your work will simplify complex processes, improve tech resiliency, and ensure high-performing, seamless solutions that power life’s most important moments.

In this dynamic environment, you’ll collaborate with talented peers across teams and functions, expanding your skills in impactful ways. Ready to push boundaries and set new industry standards? Join us and help drive the future of technology forward. MetLife Ireland which is located in both Dublin and Galway City Centres. Dublin is our Operational Headquarters for our Europe Region. In addition, both Dublin and Galway have a significant number of our colleagues that are focused on supporting our broader EMEA and Global functions.

They are homes to our Actuarial, Audit, Compliance, Finance, IT, Legal, Operations, Risk and Treasury Teams. The Opportunity The ML Ops Engineer is responsible for designing, building, automating, deploying, monitoring, and supporting machine learning solutions across the enterprise. The role bridges Data Science, Data Engineering, DevOps, Cloud Engineering, and Architecture teams to ensure machine learning models can be reliably deployed and operated in production.

The candidate will establish and support enterprise ML Ops capabilities, including CI/CD pipelines, model deployment frameworks, feature management, model monitoring, governance controls, and automated retraining processes.

Key Responsibilities

ML Ops Platform Engineering Design and implement enterprise ML Ops platforms and frameworks. Build reusable ML deployment patterns and automation. Develop model serving and inference architectures. Create standardised deployment templates and engineering guardrails. Support containerisation and orchestration platforms. CI/CD & Automation Build and maintain CI/CD pipelines for machine learning workloads. Automate model validation, testing, deployment, promotion, and rollback processes.

Implement Infrastructure-as-Code (IaC) practices. Establish release management standards for ML solutions. Model Operationalisation Convert experimental data science solutions into production-grade systems. Package, deploy, version, and manage ML models. Manage model registries and artefact repositories. Support batch, real-time, and API-based inference patterns.

Monitoring & Reliability Implement monitoring for: Model performance Data quality Data drift Concept drift Operational health Establish alerting and observability frameworks. Support SLA/SLO compliance for AI and ML services. Cloud & Platform Infrastructure Build and maintain cloud-native ML platforms. Manage compute environments for training and inference workloads. Optimise platform scalability, performance, reliability, and cost.

Implement secure access controls and secrets management. Governance, Risk & Compliance Ensure compliance with enterprise AI governance standards. Support Responsible AI (RAI) requirements. Implement auditability, reproducibility, lineage, and traceability controls. Maintain model documentation and governance evidence.

Key Stakeholders

Data Scientists Data Engineers Cloud Engineers Solution Architects Information Security teams Business stakeholders

Required Qualifications

5-8 years of IT experience. 3+ years in ML Ops, ML Engineering, Data Engineering, or Platform Engineering. Experience in Azure ML, Domino, Azure Databricks, ADF Good understanding of supervised learning, unsupervised learning, model evaluation, feature engineering, model life cycle management Experience deploying machine learning solutions into production. Experience with cloud-native data and AI platforms. Experience implementing CI/CD pipelines.

Strong analytical skills with demonstrated ability to apply analysis to actionable insights Excellent verbal and written communication skills with focused attention to details, as well as demonstrated professionalism and time/task management skills

Preferred Qualifications

Financial Services, Insurance, or highly regulated industry experience. Experience supporting enterprise-scale AI platforms. Exposure to GenAI, LLMOps, and Agentic AI frameworks. The expected salary range for this position is €56500 - €75300. This role may also be eligible for annual short-term incentive compensation. All incentives and benefits are subject to the applicable plan terms.