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.