Senior Machine Learning Engineer

• 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.

• 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.

• 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

At Coforge, we hire professionals based solely on their skills and qualifications and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.