Staff Machine Learning Operations Engineer
New
Brazil, Aligned with your time zoneFull-TimeStaff
SalaryAt least 7,500 USD per month
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSDockerPythonApache AirflowGCPKubernetesAzureCI/CDTerraformMLOps
Requirements
- 6+ years of experience in MLOps, DevOps, or data engineering roles within production environments.
- Strong programming skills in Python (primary), with additional experience in Java, Scala, or similar languages.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP, including compute, storage, and orchestration services.
- Deep understanding of Kubernetes, Docker, and containerized deployment environments.
- Experience building and maintaining CI/CD pipelines and Infrastructure-as-Code (Terraform or similar tools).
- Strong knowledge of data pipeline orchestration tools such as Apache Airflow or equivalent.
- Experience working with ML frameworks and, preferably, exposure to LLMs or tools such as LangChain.
- Strong analytical and problem-solving skills with a proactive, ownership-driven mindset.
- Excellent communication skills and ability to collaborate across technical and non-technical teams.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related fields.
Responsibilities
- Lead the design, development, and maintenance of scalable MLOps infrastructure for deploying, monitoring, and managing ML models in production environments.
- Build and automate end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, training, evaluation, and deployment.
- Partner with data science teams to translate model requirements into production-ready systems and optimize performance in real-world environments.
- Implement and maintain CI/CD pipelines to ensure reproducible, efficient, and reliable model delivery.
- Design monitoring, logging, and alerting systems to track model performance, system health, and operational risks.
- Optimize cloud infrastructure for scalability, reliability, cost efficiency, and performance across distributed systems.
- Provide technical leadership, mentorship, and guidance to engineers while driving continuous improvement and innovation in MLOps practices.
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