Mid Machine Learning Engineer
Summary
Remote (LatAm hiring locations) mid-level Machine Learning Engineer who designs, trains, deploys, and monitors production ML models and pipelines for multi-terabyte clinical and consumer datasets. Core stack: Python, SQL, PySpark, scikit-learn, XGBoost, PyTorch/TensorFlow, MLflow, Databricks, and cloud platforms.
Job Title: Mid Machine Learning Engineer
Key Skills: Python, SQL, PySpark, Machine Learning, MLflow, Databricks, scikit-learn, XGBoost, PyTorch, TensorFlow, Cloud Platforms
Experience: 3+ YOE.
Location: Open to candidates from approved hiring locations.
Mode: Remote.
We at Coforge are hiring Mid Machine Learning Engineer (#15311-1-3) with the following skill set.
Key Responsibilities
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Design, develop, deploy, and maintain scalable production-grade machine learning models and pipelines in cloud environments.
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Architect and optimize data ingestion pipelines, feature engineering, and label sets for multi-terabyte clinical and consumer datasets.
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Manage the end-to-end ML lifecycle, including experimentation, training, validation, versioning, deployment, monitoring, and continuous model improvement.
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Collaborate with product, engineering, and business stakeholders to translate requirements into measurable ML solutions and rapidly prototype emerging ML technologies.
Required Skills & Qualifications
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3+ years of experience as an ML Engineer, Data Scientist, or Data Engineer focused on building and deploying ML pipelines and models.
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Strong proficiency in Python, SQL, and PySpark for large-scale data processing.
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Hands-on experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
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Experience with ML pipeline and model management tools such as MLflow or equivalent.
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Experience working with cloud-based environments including AWS, Google Cloud, Azure, or Databricks.
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Proven experience managing large datasets with a focus on data quality, scalability, and model training best practices.
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Experience with the full ML lifecycle, including experimentation, validation, deployment, monitoring, and model versioning.
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Strong software engineering fundamentals with ability to write clean, maintainable, reusable, and well-documented code.
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Strong communication and collaboration skills across technical and non-technical teams.
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Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
Preferred Skills:
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Experience with healthcare claims, EHR, or life sciences datasets.
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Advanced degree (M.S. or Ph.D.) in Computer Science, Data Science, or a related technical field.
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Knowledge of MLOps practices, including CI/CD for ML, model versioning, and deployment.
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Experience with deep learning approaches for time series forecasting.
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Knowledge of time series forecasting, causal inference, or large language models.
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Experience defining success metrics with product managers or analysts.
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Experience working in fast-paced Agile environments.
Posted On: 24-09-2026
At Coforge, we hire professionals based solely on their skills and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.
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