MLOps Engineer

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

As an MLOps Engineer, you will design, implement, and maintain automated environments for data products and machine learning models. You will operate at the intersection of Data Engineering, Data Science, and Operations, helping teams move models reliably from experimentation into production. The role focuses heavily on Azure-based data and AI platforms, with responsibility for automation, governance, scalability, and operational reliability. You will work with technologies including Azure Databricks, Spark, Azure Machine Learning, MLflow, Docker, and Kubernetes. Your work will directly support the continuous evolution of analytics and AI solutions in enterprise environments. This is an opportunity to contribute to sophisticated data and AI initiatives while helping establish robust engineering and deployment practices.

Accountabilities:

• 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.

Requirements:

• 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.

Benefits:

• 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.