MLOps Engineer

Join our team as an MLOps Engineer at the Buenos Aires Center of Excellence!

At MetLife, we are looking for an MLOps Engineer to strengthen our operational and engineering capabilities within the Data & Analytics organization. In this role, you will ensure that our Machine Learning models and AI initiatives deliver scalable, reliable, and automated value across the LATAM region.

In This Role, You Will

• Design, implement, and maintain end-to-end Machine Learning pipelines (data ingestion, training, validation, deployment, and monitoring).
• Operationalize AI/ML models, including LLM-based solutions, within the Azure ecosystem and corporate platforms. Azure cloud experience mandatory.
• Apply best practices for version control, CI/CD, automated testing, and model lifecycle governance.
• Manage infrastructure for model execution (containers, APIs, microservices, orchestration, autoscaling).
• Define and enforce standards for reproducibility, traceability, and model observability.
• Automate key steps of the ML lifecycle to accelerate robust and scalable model releases.
• Implement AIOps practices to monitor models and systems in production, including performance, drift, cost efficiency, and anomaly detection.
• Build and configure alerts, dashboards, and monitoring tools in partnership with IT and Data Management, DevOps.
• Manage the operational lifecycle of Machine Learning and generative models (LLMs), including fine-tuning, evaluation, RAG pipelines, and quality monitoring.
• Ensure compliance with internal policies on data governance and models, security, risk, privacy, and regulatory standards.

To Be Successful In This Role, You Need

• Academic background in Engineering, Computer Science, Information Systems, STEM, or related fields.
• Proven experience in MLOps, AIOps, data engineering for ML, or similar roles.
• Proven experience with platforms like Databricks, Domino, Azure ML Studio, MLflow.
• Hands‑on experience deploying Machine Learning models into production.
• Strong knowledge of Python, Git, CI/CD, Docker, and model orchestration platforms (Azure ML, Kubernetes, ACI, AKS).
• Experience working with cloud‑based pipelines (preferably Azure).
• Knowledge of SQL and experience working with data lakes and data warehouses.
• Familiarity with ML frameworks such as Scikit-learn, PyTorch, TensorFlow.
• Intermediate/advanced English skills (reading, writing, speaking).
• Experience with Agile/Scrum methodologies and tools such as Azure DevOps or GitHub.
• Understanding of monitoring, logging, observability, and system performance.

It Is a Plus If You Also Have

• Knowledge of monitoring tools such as Grafana, Prometheus, App Insights, Kibana, MLflow Monitoring.
• Experience implementing feature stores, model registries, or large‑scale ML architectures.
• Experience integrating APIs, microservices, or serverless architectures.
• Strong analytical thinking, communication skills, business acumen, and the ability to work with multidisciplinary teams.