Data Engineer

Responsibilities
Design, train, and deploy machine learning models and AI algorithms to extract actionable insights from structured and unstructured datasets, including sensor data, APIs, and databases.Develop and optimize end-to-end machine learning workflows (MLOps), encompassing data preprocessing, feature engineering, model training, and deployment.Apply advanced statistical analysis and predictive modeling to operational and system data to drive product innovation, enhance system performance, and automate decision-making.Collaborate with engineering teams to build and maintain the data pipelines and architectures necessary to support scalable AI solutions and downstream analytics.Perform rigorous model validation, A/B testing, and anomaly detection to identify data drift, troubleshoot performance degradation, and ensure the reliability of AI system outputs.Optimize machine learning model inference and training performance across cloud platforms and distributed computing environments.Document model architectures, experimental results, and data definitions to ensure reproducibility, ethical AI practices, and cross-team knowledge sharing.Support the deployment, scaling, and lifecycle management of ML models within cloud environments, ensuring robust integration, scalability, and security.Qualifications
Bachelor's Degree in Data Science, Computer Science, Statistics, or a related quantitative field;
Master's or Ph.D. preferred.Strong understanding of machine learning algorithms (supervised/unsupervised learning, neural networks, etc.), statistical modeling, and data processing workflows.Proficiency in Python and extensive experience with industry-standard ML/AI frameworks and libraries (e.G., TensorFlow, PyTorch, Scikit-Learn, Pandas).Solid SQL skills and experience extracting and manipulating large-scale structured and unstructured datasets for model training.Familiarity with cloud platforms (e.G., GCP, AWS) and MLOps infrastructure tools for model deployment and orchestration.Ability to work in a fast-paced, exploratory environment and take initiative in identifying new AI use cases.Keen awareness of and interest in staying current with the latest advancements in artificial intelligence and machine learning research.Strong ability to communicate complex data science concepts and model results effectively to both technical and non-technical stakeholders during the R&D process.
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