Data Engineer

Job Description

Job Details Category: Data Science Location : Bangalore Experience Level: 4-8 Years Position Description We are looking for a Data Engineer who will play a pivotal role in transforming raw data into actionable intelligence through sophisticated data pipelines and machine learning deployment frameworks. They will collaborate across functions to understand business objectives, engineer data solutions, and ensure robust AI/ML model deployment and monitoring. This role is ideal for someone passionate about data science, MLOps, and building scalable data ecosystems in cloud environments. Key Responsibilities Data Engineering & Data Science: Preprocess structured and unstructured data to prepare for AI/ML model development. Apply strong skills in feature engineering, data augmentation, and normalization techniques. Manage and manipulate data using SQL, NoSQL, and cloud-based data storage solutions such as Azure Data Lake. Design and implement efficient ETL pipelines, data wrangling, and data transformation strategies. Model Deployment & MLOps Deploy ML models into production using Azure Machine Learning (Azure ML) and Kubernetes. Implement MLOps best practices, including CI/CD pipelines, model versioning, and monitoring frameworks. Design mechanisms for model performance monitoring, alerting, and retraining. Utilize containerization technologies (Docker/Kubernetes) to support deployment and scalability Business & Analytics Insights Work closely with stakeholders to understand business KPIs and decision-making frameworks. Analyze large datasets to identify trends, patterns, and actionable insights that inform business strategies. Develop data visualizations using tools like Power BI, Tableau, and Matplotlib to communicate insights effectively. Conduct A/B testing and evaluate model performance using metrics such as precision, recall, F1-score, MSE, RMSE, and model validation techniques. Desired Profile Proven experience in data engineering, AI/ML data preprocessing, and model deployment. Strong expertise in working with both structured and unstructured datasets. Hands-on experience with SQL, NoSQL databases, and cloud data platforms (e.g., Azure Data Lake). Deep understanding of MLOps practices, containerization (Docker/Kubernetes), and production-level model deployment. Technical Skills Proficient in ETL pipeline creation, data wrangling, and transformation methods. Strong experience with Azure ML, Kubernetes, and other cloud-based deployment technologies. Excellent knowledge of data visualization tools (Power BI, Tableau, Matplotlib). Expertise in model evaluation and testing techniques, including A/B testing and performance metrics. Soft Skills Strong analytical mindset with the ability to solve complex data-related problems. Ability to collaborate with cross-functional teams to understand business needs and provide actionable insights. Clear communication skills to convey technical details to non-technical stakeholders. If you are passionate to work in a collaborative and challenging environment, apply now!