Key Responsibilities

• Design, build, and deploy machine learning models using TensorFlow and/or PyTorch
• Develop and maintain end-to-end ML pipelines and CI/CD workflows
• Implement MLOps best practices including model versioning, monitoring, and retraining
• Build and manage cloud-based ML infrastructure on AWS, Azure, or GCP
• Containerize and orchestrate ML workloads using Docker and Kubernetes
• Perform data processing and feature engineering using Python, NumPy, Pandas, and Scikit-learn
• Contribute to ML system design and provide technical leadership on ML initiatives
• Monitor deployed models for performance, drift, and reliability

Requirements

• Strong hands-on experience with Python (NumPy, Pandas, Scikit-learn)
• Proficiency in TensorFlow and/or PyTorch
• Experience with MLOps, CI/CD, and ML pipeline development
• Experience with at least one major cloud platform (AWS, Azure, or GCP)
• Experience with Docker and Kubernetes
• Experience with ML system design and model monitoring
• Preferred: MLflow, SageMaker, or Azure ML
• Preferred: Spark or Ray experience
• Preferred: experience with LLMs, Transformers, and vector databases
• Preferred: prior technical leadership experience

Skills Required