Software Engineer II - MLOps in Brazil, Indiana at Jobgether

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

This role sits at the core of operationalizing machine learning systems in production, bridging the gap between data science experimentation and scalable, reliable software engineering. You will work in a highly collaborative, global engineering environment focused on building infrastructure that enables ML models to run efficiently, safely, and at scale. The position involves designing and maintaining end-to-end ML pipelines, ensuring models are continuously deployed, monitored, and optimized in production. You will contribute directly to system reliability, performance, and cost efficiency while working closely with data scientists, backend engineers, and infrastructure teams. This is a hands-on engineering role where automation, observability, and scalability are key priorities. You will help transform ML models into real-world product capabilities that directly support operational businesses and their customers.

• 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.

• 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.

• Remote-first work environment with global collaboration across distributed teams.
• Flexible working hours supporting work-life balance and autonomy.
• Self-managed PTO allowing full control over personal time off.
• Monthly compensation starting from USD $4,500.
• Home office support including equipment choice (Mac or PC) and setup stipend.
• Culture of innovation with strong emphasis on learning, experimentation, and career growth.
• Inclusive, mission-driven engineering culture focused on meaningful real-world impact.