MLOps Engineer

Key Responsibilities:

Model Development: Develop and maintain machine learning models using Python and relevant libraries.

Data Handling: Prepare data, train models, and oversee deployment and monitoring processes. Containerization: Implement containerization solutions using Docker and manage orchestration with Kubernetes.

CI/CD Management: Develop and manage CI/CD pipelines using tools like Jenkins for efficient deployment.

Version Control: Utilize Git for effective version control and codebase management.

Cloud Deployment: Deploy and scale machine learning models on cloud platforms (Google Cloud, Azure, AWS).

ETL and Data Pipelines: Implement ETL processes and manage data pipelines using tools like Apache Spark and Apache Airflow.

Catalog Maintenance: Keep 7 models in the catalogue up to date with thousands of features, ensuring they meet client needs.

Mandatory Skills:

• Proficient in Python programming.
• Strong understanding of machine learning lifecycle and libraries such as Scikit-learn, Keras, Pandas, XGBoost, LightGBM.
• Experience with Docker for containerization and Kubernetes for orchestration.
• Familiarity with CI/CD tools like Jenkins.
• Proficient in BigQuery, PySpark, SQL, Apache Hadoop, HDFS, and Hive.
• Expertise in cloud platforms (Google Cloud, Azure, AWS).
• Knowledge of ETL processes, data pipelines, and workflow automation tools like Apache Spark and Apache Airflow.
• Experience with MLflow for model management.

Preferred Skills:

• Experience with feature management tools like Feast.
• Understanding of A/B testing, canary deployments, and model drift monitoring.
• Proficiency in monitoring and logging tools such as Prometheus and Grafana.
• Familiarity with model interpretability tools like SHAP.
• Knowledge of advanced machine learning and deep learning frameworks (e.g., TensorFlow, Keras, PyTorch).
• Ability to design cost-efficient pipelines and optimize resource usage in cloud environments.