Key Responsibilities:
Machine Learning Pipeline Development
- Design and implement scalable ML pipelines using Azure ML, Databricks, and PySpark.
- Develop reusable ML workflow templates to streamline model training, validation, and deployment.
- Ensure pipeline efficiency, scalability, and reliability across environments.
Model Development & Statistical Analysis
- Apply statistical techniques including hypothesis testing (T-Test, Z-Test), regression models (linear and logistic), and classification algorithms.
- Build ML models using frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras, CNTK, and MXNet.
- Develop forecasting solutions using ARIMA, ARIMAX, and exponential smoothing techniques.
- Apply probabilistic models, graph-based models, and similarity metrics (Euclidean, Manhattan, Hamming).
MLOps & CI/CD Implementation
- Build and maintain CI/CD pipelines using GitHub and GitHub Actions.
- Integrate code quality and security tools such as SonarQube.
- Automate deployment and monitoring of ML models across environments.
Model Deployment & Cloud Engineering
- Containerize and deploy ML models using Azure Kubernetes Service (AKS).
- Design and manage scalable APIs for model inference and integration with enterprise applications.
- Ensure high availability, scalability, and reliability of deployed ML systems.
Model Monitoring & Optimization
- Monitor model performance, data drift, and data quality using tools such as Evidently AI and Great Expectations.
- Perform model optimization and retraining strategies based on performance metrics.
- Implement cost optimization strategies for training and inference workloads.
Collaboration & Stakeholder Management
- Work closely with data scientists, DevOps engineers, and IT teams to operationalize ML solutions.
- Translate research models into production-ready systems.
- Support cross-functional integration of AI capabilities into business applications.
Documentation & Best Practices
- Maintain detailed documentation of ML pipelines, APIs, and deployment workflows.
- Define best practices for scalable, reusable, and maintainable ML systems.
- Support knowledge sharing across teams.
