MLOps Engineer - Capital Federal, Argentina

The Opportunity

The MLOps Engineer is responsible for building and maintaining the operational foundation that enables scalable, reliable, and secure Machine Learning and AI solutions across Latin America. Guided by our purpose, always with you, building a more confident future, and our New Frontier strategy focused on stronger growth, attractive returns, and all-weather performance, this is an exciting opportunity to help accelerate MetLife’s AI transformation by bringing models and intelligent solutions from experimentation to production at scale.

As part of the Data & Analytics organization within the Buenos Aires Center of Excellence, this role will drive the automation, governance, deployment, and monitoring of Machine Learning and Generative AI solutions. The position will play a critical role in ensuring that AI initiatives deliver sustainable business value through robust engineering practices, operational excellence, and cloud-native technologies.

Key Responsibilities

• Design and maintain end-to-end Machine Learning and AI pipelines, including data ingestion, model training, validation, deployment, monitoring, and lifecycle management across enterprise platforms.
• Develop and manage scalable infrastructure and automation capabilities using cloud-native technologies, CI/CD practices, containers, APIs, orchestration platforms, and model governance frameworks.
• Implement MLOps and AIOps best practices to ensure model reliability, observability, reproducibility, performance monitoring, drift detection, cost efficiency, and operational excellence in production environments.
• Partner with Data Management, DevOps, Technology, and Analytics teams to build monitoring solutions, establish engineering standards, and accelerate the deployment of Machine Learning and Generative AI use cases.
• Ensure compliance with data governance, security, privacy, risk management, and regulatory requirements while supporting the operational lifecycle of Machine Learning and LLM-based solutions.
  
  

Required Qualifications

• Bachelor's degree in Engineering, Computer Science, Information Systems, STEM disciplines, or a related field, combined with proven experience in MLOps, AIOps, data engineering for Machine Learning, or similar technical roles.
• Hands-on experience deploying and operating Machine Learning models in production environments using platforms such as Azure Machine Learning, Databricks, Domino, MLflow, or related technologies.
• Strong technical proficiency in Python, Git, CI/CD, Docker, SQL, cloud-based architectures, and model orchestration platforms, with demonstrated experience in Azure cloud environments.
• Demonstrated proficiency in leveraging AI and Generative AI technologies, including LLM-based solutions, to support scalable model operations, automation, monitoring, and business value delivery.
• Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively across multidisciplinary teams in an Agile environment and communicate in English at an intermediate to advanced level.
  
  

Preferred Qualifications

• Experience with Kubernetes, AKS, ACI, feature stores, model registries, large-scale Machine Learning architectures, and cloud-native AI platforms.
• Experience implementing observability and monitoring solutions using tools such as Grafana, Prometheus, Kibana, Application Insights, or MLflow Monitoring.
• Experience integrating APIs, microservices, serverless architectures, or advanced Generative AI capabilities such as Retrieval-Augmented Generation (RAG), model fine-tuning, and LLM evaluation frameworks.