Qualifications Masters degree in Software Engineering, Data Engineering, Computer Science or related field 5 years of relevant work experience Strong Scala and Python background Experience with Apache Spark and/or Ray Knowledge of AWS, GCP, Azure, or other cloud platform Knowledge of current principles and frameworks for ML Ops Experience with ML Ops technologies such as ML Flow, DVC, Grafana, DataHub, Databricks Experience with machine learning technologies such as PyTorch, TensorFlow, AWS Sagemaker Experience with CI/CD pipelines, including Jenkins or Git Actions Experience with Docker containerization or Kubernetes orchestration Experience in improving data security and privacy, and managing and reducing cloud costs Knowledge of API development and machine learning deployment Responsibilities Develop and implement a strategy for continuous improvement of our Machine Learning Ops including versioning, testing, automation, reproducibility, deployment, monitoring, and data privacy Develop and report on ML Ops metrics such as deployment frequency, lead time for changes, mean time to restore, and change failure rate Collaborate with data scientists, data engineers, API engineers, and the dev ops team Build scalable data ingestion and machine learning inference pipelines Scale up production systems to handle increased demand from new products, features, and users Provide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costs Automate and handle the life-cycle of the systems and platforms that process our data #J-18808-Ljbffr