Machine Learning Solutions Architect

At least 6 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer. 4-year Bachelor's degree in Computer Science or related field. Experience deploying machine learning models in a production setting. Expertise in Python, Scala, Java, or another modern programming language. Ability to build and operate robust data pipelines. Strong working knowledge of SQL and ability to write, debug, and optimize distributed SQL queries. Hands-on experience in one or more big data ecosystem products/languages (e.g., Spark, Snowflake, Databricks). Familiarity with multiple data sources (e.g., JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP). Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), and storage systems (e.g., AWS, Databricks, Cloudera). Production experience in core data technologies (e.g., Spark, HDFS, Snowflake, Databricks, Redshift, & Amazon EMR). Development of APIs and web server applications (e.g., Flask, Django, Spring). Complete software development lifecycle experience. Excellent communication and presentation skills. Experience with Docker, Kubernetes, or other containerization technology (preferred). AWS Sagemaker (or Azure ML) and MLflow experience (preferred). Experience building enterprise ML models (preferred).

Design and implement data solutions for ML model inference, retraining, and monitoring. Provide thought leadership on technologies and solution design. Build and operate ML solutions in production, ensuring performance, security, and scalability. Create environments for data scientists to build and manipulate data. Define model deployment approach and infrastructure. Partner with data scientists to ensure solution deployability and maintainability. Create operational testing strategies and validate models. Ensure quality of delivered product.