Machine learning engineer senior

Descripción del cargo

Requirements:
Must have:
• 4–6 years of experience in Machine Learning Engineering with strong expertise in MLOps and ML system architecture.
• Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Hands-on experience with MLOps platforms and orchestration tools, including MLflow, Kubeflow, AWS SageMaker, Airflow, or similar.
• Strong knowledge of cloud platforms (preferably AWS) and container technologies (Docker, Kubernetes).
• Experience implementing model monitoring, logging, performance optimization and inference pipelines.
• Solid understanding of data pipelines, APIs, distributed systems, and real-time/ batch data processing.
• Experience with AWS Data Pipeline and cloud-native ML services.
• Strong ability to work cross-functionally with Data Scientists, Engineers, and business stakeholders.
• Preferred certifications: AWS Machine Learning Specialty, Google Professional ML Engineer, or equivalent.
Nice to have:
• Systems thinking and designing scalable ML architectures.
• Ability to bridge research prototypes with production-grade systems.
• Strong collaboration and communication across technical and non-technical teams.
• Analytical mindset focused on automation, reliability, and continuous improvement.
• Ability to adopt emerging MLOps tools and industry best practices.

Responsibilities:
• Design, implement, and manage end-to-end ML system architecture, supporting model training, deployment, monitoring, and scaling.
• Build and optimize MLOps pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and inference.
• Implement frameworks for model versioning, experiment tracking, and reproducibility.
• Manage model I/O flows, ensuring high availability, performance, and robustness in production environments.
• Collaborate closely with Data Scientists to operationalize research models into production-ready ML solutions.
• Integrate CI/CD practices into ML workflows for automated testing, deployment, and retraining.
• Monitor ML models in production, detect data drift, and trigger automated retraining pipelines when required.
• Establish and maintain governance, security, compliance, and ethical AI practices across ML workflows.
• Contribute to architectural decisions and continuously improve MLOps capabilities and system reliability.

Required Languages 
Advance English 80-95%

Location
Remote - Colombia