Senior 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. Preferred Skills · Experience working with healthcare datasets, including claims, eligibility, pharmacy, or EHR data. · Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, Machine Learning, or a related field. · Strong understanding of MLOps practices, including CI/CD for Machine Learning, model versioning, and automated retraining. · Experience with deep learning techniques for time series forecasting, sequential data, or hierarchical modeling. · Experience designing model evaluation frameworks, experimentation protocols, and performance metrics. · Familiarity with Kubernetes-native ML platforms such as Kubeflow, KServe, or Airflow on Kubernetes. · Experience working in fast-paced, high-growth environments with multiple concurrent priorities.

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.

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. Preferred Skills · Experience working with healthcare datasets, including claims, eligibility, pharmacy, or EHR data. · Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, Machine Learning, or a related field. · Strong understanding of MLOps practices, including CI/CD for Machine Learning, model versioning, and automated retraining. · Experience with deep learning techniques for time series forecasting, sequential data, or hierarchical modeling. · Experience designing model evaluation frameworks, experimentation protocols, and performance metrics. · Familiarity with Kubernetes-native ML platforms such as Kubeflow, KServe, or Airflow on Kubernetes. · Experience working in fast-paced, high-growth environments with multiple concurrent priorities. Posted On: 09-10-2026