Senior Machine Learning Engineer

About the role

Coforge is hiring a Senior Machine Learning Engineer to lead the design and delivery of scalable, production-grade machine learning systems in cloud environments. The position is remote and open to candidates based in Colombia.

Key responsibilities

• Lead the design and implementation of scalable, production-grade ML systems in cloud environments.
• Architect and deliver end-to-end ML solutions, from data ingestion and feature engineering to deployment and monitoring.
• Design and manage containerized ML workloads using Docker and Kubernetes for model training, batch inference and real-time serving.
• Oversee large-scale data pipelines processing multi-terabyte datasets and ensure reliability and performance.
• Lead experimentation strategies, including A/B testing, model validation and lifecycle management using MLflow and Databricks.
• Drive continuous model improvement through automated retraining, model monitoring, bias mitigation and performance optimization.
• Evaluate and prototype emerging AI/ML technologies, frameworks and architectures.
• Collaborate with Product, Engineering, Data and Leadership teams to define and execute ML initiatives aligned with business goals.
• Establish engineering standards, code quality practices and technical documentation, and mentor engineers.

Required profile

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science or a related field, or equivalent practical experience.
• 5+ years of industry experience building, deploying and scaling ML systems.
• Proven experience designing and managing production ML pipelines with MLflow or similar tools.
• Experience deploying and operating ML solutions in cloud environments such as AWS, Azure, GCP or Databricks.
• Strong understanding of the end-to-end ML lifecycle and excellent communication skills.

Required skills

• Deep expertise in Python, SQL and PySpark for distributed data processing.
• Hands-on experience with Scikit-learn, PyTorch, TensorFlow and XGBoost.
• Hands-on experience with Docker and Kubernetes.
• Preferred: healthcare datasets (claims, eligibility, pharmacy, EHR), advanced degree, MLOps practices (CI/CD, model versioning, automated retraining), deep learning for time series or sequential data, and Kubeflow, KServe or Airflow on Kubernetes.