Senior Machine Learning Engineer

ASAAS

full-time

Posted on: 1/8/2026

Location Type: Remote

Location: Brazil

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About the role

Design, implement, and maintain end-to-end machine learning pipelines in production, ensuring scalability and reliability.
Develop and manage inference systems for both batch and real-time (online/live) use, optimizing latency and throughput according to business needs.
Implement and optimize MLOps infrastructure, including model versioning, retraining automation, and performance monitoring.
Collaborate with data scientists to translate experimental models into robust, production-ready solutions.
Implement CI/CD practices for ML models, ensuring safe deployments and rollbacks when necessary.
Optimize model performance in production, including latency, throughput, and compute resource usage across different inference scenarios.
Develop robust pipelines for data processing and feature engineering, optimizing the handling of large data volumes.
Manage the full model lifecycle, from experimentation to decommissioning.
Stay up to date with ML Engineering best practices and propose continuous improvements to infrastructure and processes.

Degree or equivalent practical experience in Computer Science, Software Engineering, Information Systems, or a related field.
Proven experience in productizing and operating machine learning models.
Hands-on experience with different inference strategies: batch processing and real-time serving.
Experience with monitoring techniques for model drift and data drift.
Advanced proficiency in Python and its ML libraries (scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch).
Solid experience with MLOps frameworks (MLflow, Kubeflow, or similar).
Proficiency in SQL for data manipulation and analysis.
Practical knowledge of containerization (Docker, Kubernetes) and workflow orchestration.
Experience with APIs and system integrations.
Experience with Databricks.
Familiarity with AWS cloud and its ML services.
Knowledge of data streaming (Kafka, Kinesis) and real-time processing.
Familiarity with code and model versioning practices (Git or similar).
Ability to work with large datasets and optimize performance.
Nice-to-have: Experience in fintechs, payments, or financial institutions; knowledge of Terraform; experience with generative AI and Retrieval-Augmented Generation (RAG) techniques.

Benefits

Health and well-being: We offer medical and dental coverage with no copay, life insurance, assistance for purchasing medications and for physical activity. In addition, Neon is our partner for employee financial wellness and Zenklub for physical and mental health (we offer 4 free monthly sessions with a therapist or nutritionist). At headquarters, we also provide quick massages.
Food and family: Our flexible meal benefit is provided via a Visa credit-style card; the balance can be used as desired. At headquarters we have free food, and for families we offer childcare assistance, parental support programs, and extended maternity and paternity leave.
Education and growth: In addition to a challenging, development-focused environment, we have an in-company training platform and provide an education allowance that subsidizes 70% of tuition for degrees and language courses, as well as purchases of courses and books, so our team never stops learning.
For quality remote work: We provide a home office allowance, work equipment, furniture assistance, and have a partnership with WOBA so employees can use coworking spaces across Brazil when desired. Take a virtual tour of our headquarters in Joinville/SC.
Extras, because the Dream Team deserves it: birthday day-off, Happy Hour allowance, referral bonuses for new hires, performance-based annual bonuses, a stock options plan, and a relaxed dress-code environment.

Tip: use these terms in your resume and cover letter to boost ATS matches.

machine learning pipelinesinference systemsMLOps infrastructureCI/CD practicesdata processingfeature engineeringPythonSQLDockerKubernetes

collaborationproblem-solvingcommunicationcontinuous improvement