Machine Learning Engineer Role

OPEN Data Jobs seeks a Machine Learning Engineer to build reproducible, scalable ML models and the infrastructure to deploy and monitor them in production. This role focuses on the model lifecycle and the platform beneath it, automating training and validation, managing features and model artifacts, optimizing inference, and implementing ML operations (MLOps). The successful candidate will help data scientists and application engineers move models into production safely.

What you'll do - Build reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts. - Develop model-serving systems and APIs with appropriate latency, throughput, availability, scaling, and rollback. - Create feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls. - Monitor data quality, drift, model performance, fairness, infrastructure health, latency, and cost; set up alerts. - Build reusable libraries, templates, environments, and delivery pipelines that streamline the path from experimentation to production.

What you bring - Strong programming and software engineering practices, including testing, version control, packaging, automation, and production debugging. - Experience with model development, evaluation metrics, feature engineering, data splitting, tuning, and understanding modeling approaches. - Experience with training and inference pipelines, containers, cloud or on-prem compute, artifact management, and automated d

Machine Learning Engineer Role

OPEN Data Jobs seeks a Machine Learning Engineer to build reproducible, scalable ML models and the infrastructure to deploy and monitor them in production. This role focuses on the model lifecycle and the platform beneath it, automating training and validation, managing features and model artifacts, optimizing inference, and implementing ML operations (MLOps). The successful candidate will help data scientists and application engineers move models into production safely.

What you'll do - Build reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts. - Develop model-serving systems and APIs with appropriate latency, throughput, availability, scaling, and rollback. - Create feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls. - Monitor data quality, drift, model performance, fairness, infrastructure health, latency, and cost; set up alerts. - Build reusable libraries, templates, environments, and delivery pipelines that streamline the path from experimentation to production.

What you bring - Strong programming and software engineering practices, including testing, version control, packaging, automation, and production debugging. - Experience with model development, evaluation metrics, feature engineering, data splitting, tuning, and understanding modeling approaches. - Experience with training and inference pipelines, containers, cloud or on-prem compute, artifact management, and automated d