Machine Learning: Ability to design and implement ML solutions within established system architectures, select appropriate model classes and loss functions aligned to business objectives, implement robust cross-validation and hyperparameter optimization strategies, productionize models via reproducible pipelines and CI/CD practices, and continuously monitor for drift, robustness issues, and system-level constraints (scalability, reliability, observability, and cost). 2-4 years Experience designing and working within big data architectures using distributed processing and storage technologies (e.g., Spark, Snowflake, Hadoop, Hive, Kafka), along with hands-on experience leveraging cloud platforms (e.g., AWS, GCP, Azure) to build scalable, reliable, and production-grade data and machine learning systems (Required).