Machine Learning Engineer

Job Title: Senior Machine Learning Engineer
Key Skills: Machine Learning, Python, PySpark, Databricks, MLOps, Kubernetes
Experience: 5+ YOE.
Location: Colombia
Mode: Remote.

We at Coforge are hiring Senior Machine Learning Engineer (#15311-1-5) with the following skill set.

Key Responsibilities
· Lead the design and implementation of scalable, production-grade Machine Learning 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 platforms such as 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.
· Mentor engineers and provide technical leadership across Machine Learning initiatives.

Required Skills & Qualifications
· 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 Machine Learning systems.
· Deep expertise in Python, SQL, and PySpark for distributed data processing.
· Hands-on experience with machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow, and XGBoost.
· Proven experience designing and managing production ML pipelines using MLflow or similar tools.
· Experience deploying and operating ML solutions in cloud environments such as AWS, Azure, GCP, or Databricks.
· Strong understanding of end-to-end ML lifecycles, including data ingestion, training, evaluation, deployment, and monitoring.
· Hands-on experience with Docker, Kubernetes, and containerized ML workloads.
· Excellent communication skills and the ability to influence cross-functional teams.