Sr. Staff Engineer, Machine Learning

Aimlroles; Reynosa; On-site; MXN 650,000 - 950,000; Full time

Job summary

Aimlroles in Reynosa, Mexico, seeks a Senior Machine Learning Engineer to build, deploy, and maintain robust ML systems across manufacturing, supply chain, and technology functions. You will collaborate with data scientists and IT to operationalize models and automate data pipelines.

You will design end-to-end ML pipelines, implement scalable serving infra, and apply MLOps practices to ensure security and compliance. Fluency in technical English is required.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 3-5 years of experience in machine learning engineering, data engineering, or software engineering.
• Hands-on proficiency with Python and machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch. Databricks experience preferred.
• Practical experience with building and deploying ML models into production environments (cloud or on-premise).
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow), DevOps practices, and containerization (Docker, Kubernetes).
• Strong skills in API development, data workflow automation, and distributed computing concepts.
• Experience working with relational and non-relational databases and data pipeline tools.
• Excellent problem-solving, communication, and teamwork skills.
• Must be fluent in technical and business English (both written and verbal)

Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines, including data ingestion, preprocessing, model training, validation, deployment, monitoring, and retraining.
• Collaborate with data scientists to translate prototypes and experimental models into production-ready solutions.
• Implement and manage model serving infrastructure (APIs, batch jobs, real-time systems) that meet business requirements for performance and scalability.
• Leverage MLOps tools and best practices for model versioning, orchestration, monitoring, and CI/CD automation.
• Troubleshoot and resolve issues related to data flows, model performance, deployment, and integration with enterprise systems.
• Ensure security, data privacy, and compliance within all deployed machine learning solutions.
• Maintain clear documentation of pipelines, systems, and troubleshooting processes.
• Lead code reviews and contribute to a culture of technical excellence and continuous improvement.

Skills

Experience 3-5 years

Python

scikit-learn

TensorFlow

PyTorch

Databricks

MLflow

Kubeflow

Docker

Kubernetes

API development

Data workflow

Distributed computing

Relational DBs

NoSQL databases

Data pipelines

Problem solving

Communication

Teamwork

English fluency

Business English

Education

BSc/MSc in CS/Engineering/Data Science

Tools

Databricks

MLflow

Kubeflow

Docker

Kubernetes

Job description

Position Overview

The Senior Machine Learning Engineer is responsible for building, deploying, and maintaining robust machine learning systems that drive business value across manufacturing, supply chain, commercial, and technology functions. This role works closely with data scientists, analytics leaders, and IT to operationalize models, automate data pipelines, and ensure scalable, reliable, and secure machine learning solutions.

Key Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines, including data ingestion, preprocessing, model training, validation, deployment, monitoring, and retraining.
• Collaborate with data scientists to translate prototypes and experimental models into production-ready solutions.
• Implement and manage model serving infrastructure (APIs, batch jobs, real-time systems) that meet business requirements for performance and scalability.
• Leverage MLOps tools and best practices for model versioning, orchestration, monitoring, and CI/CD automation.
• Troubleshoot and resolve issues related to data flows, model performance, deployment, and integration with enterprise systems.
• Ensure security, data privacy, and compliance within all deployed machine learning solutions.
• Maintain clear documentation of pipelines, systems, and troubleshooting processes.
• Lead code reviews and contribute to a culture of technical excellence and continuous improvement.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 3-5 years of experience in machine learning engineering, data engineering, or software engineering.
• Hands‑on proficiency with Python and machine learning frameworks such as scikit‑learn, TensorFlow, or PyTorch. Databricks experience preferred.
• Practical experience with building and deploying ML models into production environments (cloud or on‑premise).
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow), DevOps practices, and containerization (Docker, Kubernetes).
• Strong skills in API development, data workflow automation, and distributed computing concepts.
• Experience working with relational and non-relational databases and data pipeline tools.
• Excellent problem-solving, communication, and teamwork skills.
• Must be fluent in technical and business English (both written and verbal)

Personal Attributes

• Detail‑oriented, analytical, and proactive in troubleshooting and optimizing systems.
• Collaborative team player who values knowledge sharing and continuous learning.
• Adaptable and enthusiastic about working with rapidly evolving technologies and business needs.