We are seeking a Senior Data Science Engineer with expertise in building and operationalizing production-grade machine learning pipelines . The ideal candidate will have hands-on experience with MLFlow Databricks and Azure ML and a strong background in developing ML solutions for healthcare predictive use cases . This role will be central to embedding ML models into enterprise data workflows and ensuring their ongoing performance.
ML Pipeline Engineering : Design build and maintain scalable ML pipelines leveraging MLFlow Databricks and Azure ML.
Model Development : Develop and optimize ML models particularly for healthcare predictive analytics .
Integration : Embed ML models into KPI-driven data processing and analytics workflows .
Model Lifecycle Management : Implement model monitoring drift detection retraining and continuous improvement strategies.
Collaboration : Work closely with data engineers data scientists and business stakeholders to deploy solutions that deliver measurable impact.
6 years of experience in machine learning engineering or applied data science .
Hands-on expertise with MLFlow Databricks and Azure ML .
Strong experience in developing and deploying predictive ML models ideally within healthcare.
Proficiency in Python SQL PySpark and modern ML frameworks (TensorFlow PyTorch or Scikit-learn).
Knowledge of model monitoring drift detection and automated retraining strategies .
Familiarity with cloud-native ML architectures and CI/CD for ML (MLOps).
Strong problem-solving and collaboration skills with a focus on production-grade delivery.
Work on cutting-edge healthcare predictive analytics solutions .
Gain exposure to enterprise-scale ML pipelines on modern platforms.
Opportunity to drive end-to-end ML lifecycle ownership .
Competitive compensation and career growth in a fast-growing data-driven organization.
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