AI-ML Engineering Senior Programmer Analyst

Role Overview: You will be responsible for designing and deploying predictive models, building ML pipelines, optimizing model performance, implementing Delta Lake, enabling CI/CD for ML pipelines, and troubleshooting issues in Spark Jobs and Databricks Environment. As a Senior Programmer Analyst in AI-ML Engineering, you are expected to have strong technical expertise and experience in Python, PySpark, MLflow, and predictive modeling techniques. Key Responsibilities: - Design and deploy predictive models (e.g., forecasting, churn analysis, fraud detection) using Python/SQL, Spark MLlib, and Databricks ML. - Build end-to-end ML pipelines (data ingestion, feature engineering, model training, deployment) on Databricks Lakehouse. - Optimize model performance via hyperparameter tuning, AutoML, and MLflow tracking. - Implement Delta Lake for scalable, ACID-compliant data workflows. - Enable CI/CD for ML pipelines using Databricks Repos and GitHub Actions. - Troubleshoot issues in Spark Jobs and Databricks Environment. Qualifications Required: - 3 to 5 years of experience in AI-ML Engineering. - Strong expertise in Python, PySpark, MLflow. - Experience with predictive modeling techniques including Classification, Clustering, Regression, timeseries, and NLP. - Knowledge in DataBricks is a plus. - Familiarity with cloud platforms like Azure/AWS, Delta Lake, and Unity Catalog. - Hands-on experience with Databricks Runtime for ML, Spark SQL, and PySpark. - Understanding of MLflow, Feature Store, and Unity Catalog for governance. - Knowledge of SDLC and Agile methodologies. - Excellent oral and written communication skills. - Ability to work effectively in a team, proactive, and adaptive. Role Overview: You will be responsible for designing and deploying predictive models, building ML pipelines, optimizing model performance, implementing Delta Lake, enabling CI/CD for ML pipelines, and troubleshooting issues in Spark Jobs and Databricks Environment. As a Senior Programmer Analyst in AI-ML Engineering, you are expected to have strong technical expertise and experience in Python, PySpark, MLflow, and predictive modeling techniques. Key Responsibilities: - Design and deploy predictive models (e.g., forecasting, churn analysis, fraud detection) using Python/SQL, Spark MLlib, and Databricks ML. - Build end-to-end ML pipelines (data ingestion, feature engineering, model training, deployment) on Databricks Lakehouse. - Optimize model performance via hyperparameter tuning, AutoML, and MLflow tracking. - Implement Delta Lake for scalable, ACID-compliant data workflows. - Enable CI/CD for ML pipelines using Databricks Repos and GitHub Actions. - Troubleshoot issues in Spark Jobs and Databricks Environment. Qualifications Required: - 3 to 5 years of experience in AI-ML Engineering. - Strong expertise in Python, PySpark, MLflow. - Experience with predictive modeling techniques including Classification, Clustering, Regression, timeseries, and NLP. - Knowledge in DataBricks is a plus. - Familiarity with cloud platforms like Azure/AWS, Delta Lake, and Unity Catalog. - Hands-on experience with Databricks Runtime for ML, Spark SQL, and PySpark. - Understanding of MLflow, Feature Store, and Unity Catalog for governance. - Knowledge of SDLC and Agile methodologies. - Excellent oral and written communication skills. - Ability to work effectively in a team, proactive, and adaptive.