Machine Learning Operations Engineer
BrazilFull-TimeMiddle
SalaryAt least 4,500 USD per month
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonApache AirflowKafkaData engineeringSparkCI/CDMLOpsDistributed Systems
Requirements
- 3+ years of experience in MLOps, Data Engineering, or infrastructure-focused software engineering roles.
- Strong proficiency in Python and backend engineering principles.
- Proven experience deploying, monitoring, and maintaining machine learning models in production environments.
- Hands-on experience with workflow orchestration tools such as Apache Airflow.
- Solid understanding of distributed data processing systems such as Kafka and Spark.
- Experience building and maintaining CI/CD pipelines for automated software and ML deployments.
- Strong understanding of cloud infrastructure and distributed system design.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- Strong communication and collaboration skills in cross-functional engineering teams.
- Proactive mindset with strong attention to detail and a focus on automation and reliability.
- Experience using AI tools to improve engineering productivity and workflows.
Responsibilities
- Design, build, and maintain scalable infrastructure for deploying, monitoring, and managing machine learning models in production environments.
- Develop and optimize end-to-end ML pipelines, including feature engineering, model training workflows, deployment automation, and continuous evaluation systems.
- Collaborate with data scientists and product engineers to operationalize machine learning models and ensure production readiness.
- Implement and maintain CI/CD pipelines that support reliable, automated, and reproducible ML model releases.
- Build robust monitoring, logging, and alerting systems to ensure model health, system performance, and rapid issue detection.
- Improve system architecture for scalability, reliability, uptime, and cost efficiency in distributed environments.
- Research and integrate emerging MLOps tools, frameworks, and best practices to continuously enhance platform capabilities.
- Document technical standards, operational procedures, and architectural decisions to support engineering alignment and knowledge sharing.
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