Machine Learning Solutions Architect

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Requirements:

• At least 6 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer.
• 4-year Bachelor's degree in Computer Science or a related field.
• Experience deploying machine learning models in a production setting.
• Expertise in Python, Scala, Java, or another modern programming language.
• The ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets.
• Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries.
• Hands-on experience in one or more big data ecosystem products/languages such as Spark, Snowflake, Databricks, etc.
• 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, including design, documentation, implementation, testing, and deployment.
• Excellent communication and presentation skills; previous experience working with internal or external customers.

Responsibilities:

• Designing and implementing data solutions best suited to deliver on our customer needs — from model inference, retraining, monitoring, and beyond — across an evolving technical stack.
• Providing thought leadership by recommending the technologies and solution design for a given use case, from the application layer to infrastructure.
• Create environments for data scientists to build models and manipulate data.
• Work within customer systems to extract data and place it within an analytical environment.
• Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop.
• Demonstrate the business value of data by working with data scientists to manipulate and transform data into actionable insights.
• Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines.
• Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment.
• Ensure the quality of the delivered product.