Job Level
Senior
Tech Stack
CloudPythonPyTorchScikit-LearnSQLTensorflow
About the role
- Build and automate robust, scalable ML pipelines for training, validation, deployment, and monitoring in production environments.
- Collaborate with data scientists, engineers, and product managers to understand business requirements and translate them into ML solutions.
- Analyze and process large-scale datasets to develop efficient ML models and drive actionable insights.
- Own the end-to-end ML model lifecycle, including versioning, deployment, performance monitoring (e.g., drift detection), recalibration, troubleshooting, and retraining.
- Develop and manage CI/CD pipelines for ML workflows, including automated testing, containerization, and model registry integration.
- Implement and manage scalable ML infrastructure for data processing, training, and inference in collaboration with DevOps and engineering teams.
- Continuously improve engineering practices, code quality, and automation, while staying up to date with the latest trends in AI, ML, and MLOps.
Requirements
- Minimum 5 years of experience in ML engineering or a combination of ML engineering and data science, with a focus on data analysis, feature engineering, model development, deployment, and maintenance.
- Strong hands-on experience with Databricks for ML model development and deployment.
- Strong understanding of machine learning and data science fundamentals (e.g., supervised and unsupervised learning, feature selection, etc.).
- Proficiency with popular ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience implementing MLOps practices using tools such as MLflow and CI/CD platforms (e.g., GitHub).
- Deep understanding of the ML model lifecycle and production workflows.
- High proficiency in SQL and Python.
- Familiarity with cloud-based data platforms such as Snowflake or similar technologies.
- Excellent communication and interpersonal skills to collaborate across teams.
- Robust problem-solving and analytical skills.