Department Description

The Data and AI team sit within Palmetto’s Product, Design, and Engineering function and are responsible for the strategy, infrastructure, and rigor of data across the company. We build reporting, models, and products that drive impact through our business, operational, and engineering teams.

Location

This position will be based in Mexico City.

Reporting

This position will report to the VP of Data and AI

Summary of Role

Palmetto is seeking a Senior Machine Learning Engineer. As one of Palmetto’s first two Machine Learning Engineers, you will play a foundational and strategic role in establishing a lean, scalable, and best-in-class machine learning practice. This role is the technical owner of the MLOps platform. You will be responsible for designing, building, and maintaining the core MLOps infrastructure and end-to-end ML pipelines, enabling the engineering and data communities to reliably and efficiently deploy and manage models across the business. You will be responsible for mentoring model developers, staying current with best practices, and fostering a culture that embraces ML to improve all aspects of our business. This is a high-impact role that will directly contribute to Palmetto’s mission by integrating predictive intelligence into our products and operations.

Strategic & Tactical

• Lead the design and implementation of the core Machine Learning Platform (MLOPs), enabling a standardized, transparent, and scalable process for model development, testing, deployment, and monitoring.
• Ensure seamless integration of the ML platform with Palmetto's existing data platform, with a focus on feature engineering and consumption.
• Architect the platform to support diverse model inference egress points, including REST APIs, batch inference, and MCP.
• Evaluate and make build vs. buy decisions for ML platform components with a view towards value, scalability, and maintenance. Act as a technical thought leader, creating the 2-3 year ML capability roadmap.
• Establish best practices and standards for model governance, development, version control, testing, and productionization to ensure a lean and reliable ML practice.
• Collaborate closely with Data Engineers, Data Scientists, and other ML Engineers to transition R&D models into scalable, production-ready services.
• Proactively monitor the performance and data drift of deployed models, implementing automated retraining and alerting mechanisms.

Qualifications

• 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; comfort working with a data engineering stack is a plus.
• Extensive, hands-on experience architecting systems that use MLOps frameworks (e.g., Kubeflow, MLflow, Airflow), public cloud platforms (GCP preferred), and familiarity with statistical modeling toolkits (e.g., PyTorch, XGBoost, TensorFlow, VertexAI).
• An exceptional blend of technical, mathematical, and product intuition, with proven experience in end-to-end problem formulation, from stakeholder requirement gathering, through model iteration, ending with production deployment.
• Strong written and spoken communication skills, with the ability to effectively articulate complex technical concepts to both technical and non-technical audiences.
• Insatiable curiosity and love of learning.

Success Defined

• We deploy and operate an MLOps platform, including automated deployment, testing, and monitoring, within the first 6 months.
• We help analytical teams train and deploy models 50% faster than today.
• We drive demand for ML-oriented solutions from within the business and product teams.

Employment is contingent upon the successful completion of a background check.