Four (4) years of experience with: Applying machine learning techniques, including deep learning, reinforcement learning, and NLP in production environments; Machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn for model development; Cloud platforms (AWS, Azure, GCP), including containerization and orchestration tools (Docker, Kubernetes) and cloud-native ML tools (e.g., AWS SageMaker, GCP AI Platform, Azure Machine Learning); Deploying and operating machine learning models in production environments, including system architecture and fault-tolerant design for scalability, reliability, and high availability; Writing production-grade, maintainable code using object-oriented design principles, including SQL, Python, and Spark for data processing supporting model development; Distributed systems, including distributed data processing and microservices architectures used to scale machine learning models in production; MLOps practices, including continuous integration (CI), continuous delivery (CD), automated testing, and deployment pipelines for machine learning models, including data/streaming pipeline tools such as Apache Kafka, Apache Flink, and Spark Streaming; and Setting up, monitoring and logging systems for tracking model performance and resource utilization in production environments. DUTIES: Review the architecture and implementation of machine learning models that solve complex business problems, working closely with Product, Business Units, and Data Science teams.