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About the Job
">As a Machine Learning Engineer, you will be responsible for designing, building, training, and optimizing machine learning models using state-of-the-art techniques. You will work with large datasets to identify patterns and inform feature engineering.
">Data Pipeline Management
">You will collaborate with data engineers to build robust, scalable, and efficient data pipelines. This includes ensuring data quality, preprocessing, and cleaning for ML model input.
">Deployment & Monitoring
">You will deploy ML models to production environments using MLOps practices and monitor their performance in production, iterating to improve accuracy and efficiency.
">Collaboration & Integration
">You will work with cross-functional teams to integrate ML models into existing products and services. This includes translating business problems into machine learning solutions.
">Research & Innovation
">You will stay updated with the latest advancements in machine learning and AI. You will experiment with new tools, algorithms, and frameworks to continuously improve processes.
">Documentation & Communication
">You will document model development, data processes, and experiments for reproducibility. You will communicate findings and technical details to both technical and non-technical stakeholders.
">Qualifications
">To be successful in this role, you must have a Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field. You should also have proven experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
">Nice-to-Have
">Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow is a plus. Knowledge of big data tools like Apache Spark or Hadoop can also be beneficial.
