We are seeking a skilled MLOps Engineer to join our dynamic team, contributing to our innovative MLOps projects. The ideal candidate will have a robust background in machine learning, programming, and data engineering, playing a pivotal role in the lifecycle of ML models.
Responsibilities
Model Development: Develop and maintain machine learning models using Python and relevant ML libraries.
Data Handling: Manage data preparation, model training, deployment, and monitoring processes.
Containerization & Orchestration: Implement containerization using Docker and orchestrate with Kubernetes.
CI/CD Pipelines: Develop and manage CI/CD pipelines using tools like Jenkins to ensure seamless integration and delivery.
Version Control: Use Git for version control and to manage the codebase effectively.
Cloud Deployment: Deploy and scale ML models on cloud platforms such as Google Cloud, Azure, or AWS.
ETL and Data Pipelines: Implement ETL processes and data pipelines using tools like Apache Spark and Apache Airflow for workflow automation.
Mandatory Skills
Programming: Proficient in Python programming.
ML Lifecycle Knowledge: Strong understanding of the ML lifecycle and familiarity with libraries like Scikit-learn, Keras, Pandas, XGBoost, and LightGBM.
Containerization: Experience with Docker for containerization and Kubernetes for orchestration.
CI/CD Expertise: Familiarity with CI/CD pipelines and tools like Jenkins.
Big Data Technologies: Proficient in BigQuery, PySpark, SQL, Apache Hadoop, HDFS, and Hive.
Cloud Platforms: Expertise in cloud platforms such as Google Cloud, Azure, or AWS.
Data Engineering: Knowledge of ETL processes, data pipelines, and workflow automation tools like Apache Spark and Apache Airflow.
Model Management: Experience with MLflow for effective model management.
Preferred Skills
Feature Management: Experience with tools like Feast for managing features across training and serving.
Testing & Monitoring: Understanding of A/B testing, canary deployments, and model drift monitoring.
Monitoring Tools: Proficiency in monitoring and logging tools such as Prometheus and Grafana.
Model Interpretability: Familiarity with model interpretability tools like SHAP.
Deep Learning Frameworks: Understanding of advanced ML and deep learning algorithms and frameworks such as TensorFlow, Keras, or PyTorch.
Cost Optimization: Ability to design cost-efficient pipelines and optimize resource usage in cloud environments.