Machine Learning Engineer

• Design and implement machine learning pipelines in cloud environments (Azure, AWS, or GCP).
• Develop and deploy models for classification, regression, time series, recommendation, or NLP use cases.
• Work with structured and unstructured data; apply feature engineering, model tuning, and evaluation techniques.
• Package and deploy models using containerization (Docker, Kubernetes).
• Automate and monitor ML workflows using MLflow, Airflow, or cloud-native tools (SageMaker, Vertex AI, Azure ML).
• Collaborate with data scientists, engineers, and product teams to translate business problems into ML solutions.
• Contribute to MLOps practices: model versioning, CI/CD for ML, performance monitoring, and rollback strategies.

Machine Learning Engineer – Cloud, MLOps, GenAI

AI Engineer, Full-Stack

Senior ML Engineer, AI Research

Machine Learning Research Intern

ML Engineer, Video Generation

Senior MLOps Engineer