Staff Machine Learning Operations Engineer in Brazil, Indiana at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff Machine Learning Operations Engineer in Brazil.

In this senior, highly impactful engineering role, you will help design and scale the infrastructure that powers machine learning systems in production for a global, fast-growing technology environment. You will work at the intersection of DevOps, data engineering, and applied ML, enabling teams to deploy and operate models reliably and efficiently at scale. The role focuses on building robust MLOps platforms, improving automation across the ML lifecycle, and ensuring performance, observability, and cost efficiency in production systems. You will collaborate closely with data scientists, software engineers, and product teams to translate model requirements into scalable infrastructure. This is a hands-on technical leadership position with strong influence over architecture and engineering standards. You will also mentor engineers while driving best practices in CI/CD, cloud infrastructure, and ML deployment. The environment is remote-first, highly collaborative, and innovation-driven.

In this role, you will be responsible for building and evolving the core infrastructure that supports machine learning at scale, ensuring reliability, automation, and performance across the full ML lifecycle.

• 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.

The ideal candidate combines strong software engineering foundations with deep expertise in MLOps, cloud infrastructure, and large-scale data systems, along with strong communication and leadership skills.

• 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.

• Competitive compensation starting at USD 7,500 monthly
• Fully remote-first work environment with global collaboration
• Flexible working hours aligned with your time zone
• Self-managed PTO policy for strong work-life balance
• Home office setup support, including equipment selection (Mac or PC) and setup stipend
• Access to a global, high-performing engineering culture with strong career growth opportunities
• Opportunity to work on impactful products used by tens of thousands of businesses worldwide
• Inclusive and diverse international team environment
• Continuous learning and exposure to modern ML, cloud, and DevOps technologies