This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Operations Engineer based in Brazil.
This role is centered on building and scaling the infrastructure that brings machine learning models into reliable, high-performance production systems. You will operate at the intersection of machine learning, backend engineering, and platform infrastructure, ensuring that models move seamlessly from development to deployment. The environment is highly collaborative and distributed, requiring close partnership with data scientists, product engineers, and infrastructure teams. You will focus on automation, observability, and system reliability to ensure ML-powered features perform consistently at scale. This position offers the opportunity to work on complex distributed systems that directly impact operational efficiency for service-based businesses. It is a hands-on engineering role where your work will shape how machine learning is operationalized across production systems globally.
Accountabilities:
- 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.
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.
Benefits:
- Remote-first work environment with global collaboration across distributed engineering teams.
- Flexible working hours supporting strong work-life balance.
- Self-managed PTO allowing autonomy over personal time off.
- Competitive monthly compensation starting at USD $4,500+.
- Home office setup support, including equipment choice (Mac or PC) and a setup stipend.
- Innovative engineering culture that encourages experimentation, learning, and ownership.
- Inclusive, mission-driven environment focused on building impactful solutions for real-world users.