MLOps Engineer

Tasks

• Apply responsible AI principles
• Build data pipelines for training and inference
• Build production ready machine learning pipelines
• Containerize and orchestrate model deployment
• Debug automated workflows
• Ensure privacy, security, and regulatory compliance
• Implement model serving via APIs
• Manage model versioning and experimentation tracking
• Monitor model performance and data drift
• Perform feature engineering
• Set up CI CD pipelines for machine learning workflows
• Train evaluate machine learning models

Perks/Benefits

• N/A

Skills/Tech-stack

AWS | Apache Spark | Azure | C++ | CI/CD | Cloud platform | Data Drift | Data drift monitoring | Docker | Drift monitoring | Feature Engineering | Git | Google Cloud | Google Cloud Platform | Hyperparameter Tuning | Java | Kubernetes | Machine Learning | Microservices | Model Evaluation | NumPy | Pandas | PyTorch | Python | REST APIs | Scala | Scikit-learn | Supervised Learning | TensorFlow | Unsupervised Learning | XGBoost