Staff Machine Learning Engineer
Palmetto Clean TechnologyMexico City, Mexico City
Full Time
Posted February 5, 2026
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Job Description
The Senior Machine Learning Engineer at Palmetto will design, build, and maintain the company's MLOps platform and pipelines, enabling scalable deployment and management of machine learning models to drive impactful business and operational improvements.
Key Responsibilities
- Design, build, and maintain the core MLOps infrastructure and end-to-end ML pipelines.
- Lead the development and implementation of a standardized, scalable ML platform for model deployment and monitoring.
- Collaborate with data engineers, data scientists, and ML engineers to transition models into production-ready services.
- Monitor model performance and data drift, implementing automated retraining and alerting mechanisms.
- Create and execute a 2-3 year roadmap for ML capabilities and establish best practices for model governance and development.
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related quantitative field or equivalent experience.
- 5 years of experience in Machine Learning Engineering, Software Engineering, or Data Science with a dedicated focus on building and scaling ML systems.
- Deep expertise in Python and proficiency in SQL, Docker, CI/CD, and Infrastructure as Code.
- Hands-on experience architecting systems that use MLOps frameworks such as Kubeflow, MLflow, or Airflow.
- Experience working with public cloud platforms, with GCP preferred.
- Familiarity with statistical modeling toolkits such as PyTorch, XGBoost, TensorFlow, or VertexAI.
- Ability to design, build, and maintain core MLOps infrastructure and end-to-end ML pipelines.
- Experience evaluating and making build vs. buy decisions for ML platform components with a focus on value, scalability, and maintenance.
- Ability to act as a technical thought leader, creating a 2-3 year ML capability roadmap.
- Establishing best practices and standards for model governance, development, version control, testing, and productionization.
- Collaborating closely with Data Engineers, Data Scientists, and other ML Engineers to transition R&D models into scalable, production-ready services.
- Proactively monitoring the performance and data drift of deployed models, implementing automated retraining and alerting mechanisms.
- Strong written and spoken communication skills, with the ability to effectively articulate complex technical concepts to both technical and non-technical audiences.
- Success in deploying and operating an MLOps platform, including automated deployment, testing, and monitoring, within the first 6 months.
- Ability to help analytical teams train and deploy models 50% faster than current processes.
Benefits & Perks
Compensation/salary range not specified
Work schedule not specified
Work environment perks include unlimited PTO, medical, dental, and vision coverage, paid parental leave, retirement plans
Comprehensive benefits package
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