Software Engineer II - MLOps
New
BrazilFull-TimeMiddle
SalaryAt least 4,500 USD per month
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonApache AirflowKafkaMachine LearningData engineeringSparkCI/CDDevOpsMLOps
Requirements
- 3+ years of experience in MLOps, Data Engineering, or infrastructure-focused software engineering roles.
- Strong proficiency in Python and backend engineering principles.
- Hands-on experience deploying, monitoring, and maintaining machine learning models in distributed production systems.
- Solid understanding of workflow orchestration tools such as Apache Airflow.
- Experience with distributed data processing or streaming technologies such as Kafka or Spark.
- Proven experience building CI/CD pipelines and automated software delivery workflows.
- Familiarity with cloud-based infrastructure and modern DevOps practices.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- Strong communication and collaboration skills in cross-functional engineering environments.
- Proactive, detail-oriented mindset with a strong focus on automation and system reliability.
- Demonstrated ability to leverage AI tools to improve productivity and engineering outcomes.
Responsibilities
- Design, build, and maintain robust 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, and continuous evaluation.
- Collaborate closely with data scientists and product engineers to productionize models and ensure operational readiness.
- Build and maintain CI/CD pipelines to support automated, reliable, and reproducible machine learning deployments.
- Implement monitoring, logging, and alerting systems to ensure model performance, system reliability, and early detection of issues.
- Improve system architecture for scalability, uptime, and cost efficiency across distributed environments.
- Evaluate and integrate new tools, frameworks, and best practices to enhance the MLOps ecosystem.
- Document engineering standards, workflows, and operational procedures to support knowledge sharing and consistency across teams.
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