Senior Machine Learning Engineer

## About the Role The Senior Machine Learning Engineer position at Coforge is a critical role that involves designing, deploying, and scaling machine learning systems and end-to-end ML pipelines in production environments. This role is integral to the company's efforts to leverage machine learning for business growth and improvement. As a senior member of the team, you will be responsible for managing the complete ML lifecycle, from data ingestion to model deployment and monitoring.

The day-to-day responsibilities of this role include collaborating with cross-functional teams to deliver scalable ML solutions, building and optimizing distributed data processing workflows, and ensuring the seamless integration of ML models into cloud-based environments. Your expertise will be crucial in driving the adoption of machine learning across the organization and in enhancing the company's capabilities in data-driven decision-making.

The machine learning team at Coforge is dynamic and fast-paced, with a strong focus on innovation and continuous learning. As a Senior Machine Learning Engineer, you will have the opportunity to work with cutting-edge technologies, including PySpark, scikit-learn, TensorFlow, and Kubernetes, and to contribute to the development of best practices in machine learning engineering.

## What You Will Do - Design, deploy, and scale machine learning systems and end-to-end ML pipelines in production environments.

• Build and optimize distributed data processing workflows using Python, SQL, and PySpark.
• Manage the complete ML lifecycle, including data ingestion, training, evaluation, deployment, monitoring, and model optimization.
• Collaborate with cross-functional teams to deliver scalable ML solutions and improve model performance in cloud-based environments.
• Develop and maintain large-scale data processing workflows and algorithms.
• Work with machine learning frameworks such as scikit-learn, TensorFlow, XGBoost, and PyTorch.
• Design and manage ML pipelines using tools like MLflow or equivalent.
• Deploy models in cloud environments such as AWS, GCP, Azure, or Databricks.
• Manage end-to-end ML lifecycles at scale, including deployment and monitoring.
• Deploy and manage containerized ML workloads using Kubernetes.

## What We Are Looking For - 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 as an ML Engineer with a focus on deploying and scaling ML systems.
• Strong expertise in Python, SQL, and PySpark for distributed data processing.
• Experience with machine learning frameworks such as scikit-learn, TensorFlow, XGBoost, and PyTorch.
• Proven experience designing and managing ML pipelines using tools like MLflow or equivalent.
• Hands-on experience deploying models in cloud environments such as AWS, GCP, Azure, or Databricks.
• Experience managing end-to-end ML lifecycles at scale, including deployment and monitoring.
• Experience deploying and managing containerized ML workloads using Kubernetes.
• Strong communication skills and the ability to collaborate across technical and business teams.

## Nice to Have - Experience working with healthcare data, including medical claims, pharmacy claims, eligibility data, and EHR systems.

• Knowledge of MLOps practices including CI/CD for ML, automated retraining, and model versioning.
• Experience with deep learning architectures for forecasting, sequential data, or hierarchical modeling.
• Familiarity with Kubernetes-native ML tools such as Kubeflow, KServe, or Airflow on Kubernetes.
• Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, or a related field.

## Benefits and Perks - Competitive salary package.

• Opportunities for professional growth and career advancement.
• Collaborative and dynamic work environment.
• Access to cutting-edge technologies and tools.
• Flexible working hours and remote work options.
• Comprehensive health insurance package.
• Generous paid time off policy.
• Annual bonus and stock options.
• Ongoing training and professional development opportunities.

- Highlight your experience with machine learning frameworks and cloud environments in your resume and cover letter.

• Be prepared to provide examples of your work in designing and deploying ML pipelines, and explain your approach to model optimization.
• Show enthusiasm for continuous learning and staying updated with the latest technologies in the field.
• Emphasize your ability to work collaboratively with cross-functional teams and communicate complex technical concepts to non-technical stakeholders.
• Consider including a link to your GitHub repository or a personal project that demonstrates your machine learning skills.
• Prepare to discuss your experience with containerization using Kubernetes and your understanding of MLOps practices.
• Research the company culture and be ready to talk about how your values and work style align with those of Coforge.