Machine Learning Engineer

Primary Responsibilities • Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications. • Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments. • Design and implement data pipelines and workflows that support training, inference, and model lifecycle management. • Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows. • Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles. • Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices. • Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices. • Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities. Qualifications • Master’s degree in Computer Science, Data Science, Engineering, or a related technical field. • Experience in machine learning engineering, applied ML, or related software engineering roles. • Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn. • Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask). • Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes. • Familiarity with CI/CD pipelines, testing automation, and version control using Git. • Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred. • Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts. • Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.