Ingeniero de Aprendizaje Automático (MLOps)

Placements24; Acapulco; Hybrid; MXN 900,000 - 1,300,000; Full time

Benefits offered by this job

Fully remote

Competitive salary

Comprehensive benefits

Online courses

Conferences

Internal workshops

Job summary

Placements24 is seeking a Machine Learning Engineer focused on MLOps to join their fully remote team. You will design and maintain scalable ML pipelines, optimize training, deployment, and monitoring, and ensure robust production systems for AI solutions deployed worldwide.

The ideal candidate has 3+ years in software engineering or ML engineering, experience with CI/CD, Docker, Kubernetes, cloud services, and Python.

Qualifications

• 3+ years of experience in software engineering, DevOps, or ML engineering with a focus on MLOps.
• Proven experience building and managing CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions).
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit‑learn).
• Knowledge of monitoring tools and techniques for ML systems.
• Proficiency in programming languages such as Python.
• Understanding of machine learning concepts and the ML lifecycle.
• Strong problem‑solving and scripting skills.

Responsibilities

• Design, build, and maintain robust and scalable MLOps pipelines for automated ML workflows.
• Implement tools and processes for CI/CD/CT of ML models.
• Develop and manage infrastructure for model training, deployment, and serving.
• Implement monitoring solutions to track model performance, data drift, and system health in production.
• Automate model retraining and versioning processes.
• Collaborate with data scientists to streamline model development and experimentation.
• Ensure security and compliance requirements are met for ML systems.
• Troubleshoot and resolve issues related to ML model deployment and operationalization.
• Optimize ML pipelines for cost, performance, and scalability.
• Document MLOps processes, infrastructure, and best practices.

Skills

Python proficiency

Problem solving

Scripting

ML concepts & lifecycle

Team collaboration

Education

Bachelor's or Master's in CS/Engineering

Tools

Docker

Kubernetes

CI/CD tools (Jenkins/GitLab CI/GitHub Actions)

Cloud platforms (AWS/GCP/Azure)

Job description

About the Role

Our client is seeking a dedicated Machine Learning Engineer with a strong focus on MLOps to join their innovative, fully remote team. This role is critical for bridging the gap between machine learning model development and reliable production deployment. You will be responsible for building and maintaining scalable, automated ML pipelines, ensuring efficient model training, deployment, monitoring, and management. The ideal candidate has a solid background in software engineering and machine learning, with hands‑on experience in MLOps tools and practices. You will work collaboratively with data scientists and engineers to streamline the ML lifecycle, optimize performance, and ensure the robustness of our AI solutions deployed globally. This is a unique opportunity to shape the MLOps strategy and infrastructure for a growing AI‑focused company.

Key Responsibilities

• Design, build, and maintain robust and scalable MLOps pipelines for automated ML workflows.
• Implement tools and processes for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of ML models.
• Develop and manage infrastructure for model training, deployment, and serving.
• Implement monitoring solutions to track model performance, data drift, and system health in production.
• Automate model retraining and versioning processes.
• Collaborate with data scientists to streamline model development and experimentation.
• Ensure security and compliance requirements are met for ML systems.
• Troubleshoot and resolve issues related to ML model deployment and operationalization.
• Optimize ML pipelines for cost, performance, and scalability.
• Document MLOps processes, infrastructure, and best practices.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• 3+ years of experience in software engineering, DevOps, or ML engineering with a focus on MLOps.
• Proven experience building and managing CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions).
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit‑learn).
• Knowledge of monitoring tools and techniques for ML systems.
• Proficiency in programming languages such as Python.
• Understanding of machine learning concepts and the ML lifecycle.
• Strong problem‑solving and scripting skills.

Benefits

This fully remote position offers the ultimate flexibility in terms of work location. Our client provides a competitive salary, comprehensive benefits package, and opportunities for professional growth and skill development. You will have the chance to work with a talented, globally distributed team on impactful projects that are shaping the future of AI. The company values work‑life balance and supports continuous learning through access to online courses, conferences, and internal workshops. This is an exceptional opportunity for an MLOps expert to make a significant contribution in a remote‑first environment.