Bachelor's degree in Computer Science, Data Science, Engineering, or a related field 15+ years of experience in software or data engineering, with at least 4 years focused on ML systems or MLOps in a production environment. Demonstrated experience building or operating a shared/enterprise ML platform serving multiple teams or business units. Strong proficiency in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face). Hands-on experience with MLOps tooling: Kubeflow, MLflow, Airflow, or equivalent. Experience with cloud-native data and ML services on AWS. Working knowledge of LLMs, prompt engineering, and RAG architecture patterns. Experience with containerization and orchestration (Docker, Kubernetes). Strong understanding of data engineering concepts: pipelines, feature stores, data quality, and lineage.
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