ML Ops Engineer - Data Lake & AI Infrastructure

4+ years of experience in ML engineering, MLOps, or data infrastructure roles Proven hands-on experience with containerized open-source data tools (MinIO, Apache Iceberg, Trino, Airflow, MLflow, LangChain) Experience managing infrastructure across multiple regions, including self-hosted deployments (Kubernetes, Docker Compose, Terraform) Experience monitoring ML prediction performance, drift metrics, and pipeline tools Strong understanding of data engineering best practices

Design and deploy data lakehouse infrastructure Build and scale ML pipelines Implement data ingestion and transformation workflows Support federated querying and real-time analytics Enable retrieval-augmented generation (RAG) and LLM-powered applications Develop CI/CD pipelines for ML models, infrastructure-as-code, and data pipelines Monitor, debug, and optimize data and ML services Collaborate cross-functionally with data scientists, engineers, and analysts