AI & Data Engineer

Data Pipeline & Infrastructure Development: Design, build, and maintain scalable data systems and architectures to support AI/ML model training. This includes building real-time streaming data pipelines and automating data ingestion infrastructures.

Data Preprocessing and Cleaning: Collect, clean, and pre-process data, removing anomalies and inconsistencies. This includes feature scaling, handling missing values, and reducing dimensionality to ensure high-quality input for models.

AI/ML Model Deployment (MLOps): Transform machine learning models into actionable APIs and deploy them into production using containerization and cloud infrastructure.

Model Training and Retraining: Train, optimize, and retrain systems, automating the workflow for continuous improvement.

Data Quality & Reliability Monitoring: Implement monitoring systems to track data drift, evaluate AI model performance, and ensure reliability and security.

Collaboration and Strategy: Work with Data Scientists and Product Managers to define AI use cases, translate experimental models into production systems, and design data architecture for business goals