MLOps Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an MLOps Engineer based in Brazil.

• Design, implement, and sustain automated pipelines for data products and machine learning models across development, testing, and production environments.
• Act as a technical bridge between Data Engineering, Data Science, and Operations teams to enable reliable delivery of data and ML solutions.
• Build and maintain scalable data engineering environments using Azure Databricks and Apache Spark.
• Develop and manage data workflows across Azure Data Factory, Azure Data Lake Storage Gen2, and Azure Synapse Analytics.
• Implement DataOps practices, including source control, automated deployment, CI/CD, and environment management.
• Build and maintain CI/CD pipelines for data and machine learning projects using Azure DevOps, Azure Repos, and Azure Pipelines.
• Implement experiment tracking, model versioning, and lifecycle management using MLflow and Azure Machine Learning.
• Deploy machine learning models through real-time inference endpoints and batch endpoints, ensuring reliability and scalability.
• Package ML applications and services using Docker and manage container images through Azure Container Registry (ACR).
• Deploy and operate machine learning services using Azure Kubernetes Service (AKS).
• Implement monitoring and operational visibility using Azure Monitor, Log Analytics, or comparable observability solutions.
• Apply governance, security, and access-control practices using services such as Azure Key Vault.
• Troubleshoot deployment, infrastructure, data pipeline, and model-serving issues to maintain reliable production environments.
• Continuously improve automation, deployment workflows, infrastructure reliability, and operational processes.
• Contribute to modern data and AI architecture initiatives, including opportunities involving Lakehouse, generative AI, and LLMOps technologies.

• Solid professional experience in MLOps, Data Engineering, Cloud Engineering, DevOps, or a closely related discipline.
• Strong hands-on experience with Azure Databricks and Apache Spark.
• Advanced knowledge of the Azure ecosystem, particularly Azure Data Factory, Azure Data Lake Storage Gen2, Azure Synapse Analytics, and Azure Key Vault.
• Practical experience with Git and modern version-control strategies.
• Strong experience with Azure DevOps, including Azure Repos and Azure Pipelines.
• Proven ability to design and maintain CI/CD pipelines for data and machine learning projects.
• Experience implementing monitoring and observability with Azure Monitor, Log Analytics, or equivalent tools.
• Hands-on experience with MLflow for experiment tracking and model versioning.
• Experience with Azure Machine Learning and machine learning lifecycle management.
• Experience deploying models for both real-time inference and batch processing.
• Familiarity with Docker for application and ML workload packaging.
• Knowledge of Azure Container Registry (ACR).
• Experience with Azure Kubernetes Service (AKS) for deploying and operating machine learning services.
• Strong understanding of automation, scalability, reliability, and governance principles in cloud environments.
• Ability to collaborate effectively with data scientists, data engineers, software engineers, and operations teams.
• Strong problem-solving skills, analytical thinking, and a proactive approach to troubleshooting and continuous improvement.
• Experience with Terraform or Bicep for Infrastructure as Code is a plus.
• Knowledge of Lakehouse architectures, Delta Lake, and Unity Catalog is desirable.
• Familiarity with Prometheus and Grafana for observability is advantageous.
• Experience with Generative AI, LLMOps, or applications based on generative models is a strong plus.
• Microsoft Azure certifications such as AZ-400, DP-203, DP-100, or AI-102 are desirable.

• Flexible employment model with the option of PJ or CLT contracting, depending on the arrangement.
• Opportunity to work on a major enterprise client engagement involving data, cloud, and AI technologies.
• Hands-on exposure to a broad Microsoft Azure data and AI ecosystem.
• Opportunity to work with modern MLOps technologies including MLflow, Azure Machine Learning, Docker, and AKS.
• Exposure to large-scale data engineering and analytics environments using Databricks, Spark, Data Factory, Data Lake, and Synapse.
• Opportunity to contribute to automation, CI/CD, DataOps, observability, and cloud engineering initiatives.
• Potential exposure to emerging areas such as Generative AI and LLMOps.
• Opportunity to develop expertise across the full machine learning lifecycle, from experimentation through production deployment and monitoring.
• Professional environment focused on innovation, data-driven transformation, and scalable technology solutions.