Coforge

SeniorMachineLearningEngineer

United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Machine Learning Engineer at Coforge. Skills: Machine Learning, MLOps, Python, Cloud. Design, deploy, and scale ML systems. Build and optimize distributed data processing workflows”

What You'll Achieve.

Improve model performance in cloud-based environments

Industry & Context.

Eligibility Requirements

Experience working in fast-paced, high-impact environments with multiple priorities

What They're Looking For.

Must Have

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, 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, communication skills and the ability to collaborate across technical and business teams, Experience working in fast-paced, high-impact environments with multiple priorities

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

What You'll Do.

Build and optimize distributed data processing workflows

Manage the complete ML lifecycle

Collaborate with cross-functional teams

Deliver scalable ML solutions

Improve model performance

Deploy and scale ML pipelines

Deploy and manage containerized ML workloads

How You'll Work.

Team & Collaboration

Collaborate with cross-functional teams; Collaborate across technical and business teams

Communication Scope

communication skills; ability to collaborate across technical and business teams

Full Job Description

Job Title: Senior Machine Learning Engineer Key Skills: Python, SQL, PySpark, Machine Learning, MLOps, Scikit-learn, PyTorch, XGBoost, TensorFlow, ML Pipelines, Kubernetes, Databricks, Cloud (AWS, GCP, Azure) Experience: 5+ YOE. Location: Costa Rica Mode: Remote We at Coforge are hiring Senior Machine Learning Engineer (#20529) with the following skill set. Key Responsibilities 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. 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 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. Experience working in fast-paced, high-impact environments with multiple priorities. Preferred Skills: Experience working with healthcare data, including medical claims, pharmacy claims, eligibility data, and EHR systems. Knowledge of MLOps practices

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