ML Engineer

• 5+ years of experience as an ML Engineer, building and deploying machine learning models in production
• Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn
• Experience designing and implementing end-to-end ML pipelines (data preprocessing, training, evaluation, deployment)
• Hands-on experience with feature engineering, model optimization, and experiment tracking (MLflow, Weights & Biases)
• Experience deploying ML systems on AWS, GCP, or Azure (SageMaker, Vertex AI, custom containers, serverless)
• Strong understanding of MLOps: CI/CD for ML, model versioning, monitoring, drift detection, and automated retraining
• Experience working with structured and unstructured data (tabular, text, images, logs, time series)
• Knowledge of data pipelines using tools like Airflow, Prefect, DBT, or cloud-native orchestrators
• Familiarity with API deployment for model inference (FastAPI, Flask, Lambda endpoints, gRPC)
• Solid understanding of statistics, model validation, and performance metrics
• English proficiency (B2 or higher)

Bonus: experience with NLP, multimodal models, vector databases, or RAG architectures