Machine Learning Solutions Architect

Machine Learning Engineers are the Swiss army knives of machine learning. They’re ready for anything, and they bring all the tools to ensure that data science models see the light of day. They own the infrastructure and deployment plan—from making sure data science models can actually be built using customer data to deploying them into a production environment, and everything in between.  They provide thought leadership by recommending the right technologies and solutions for a given use case, from the application layer to infrastructure.  Machine Learning Engineers have the team leadership and coding skills (e.g. Python, Java, and Scala) to get their solutions into production — and to help ensure performance, security, scalability, and robust data integration.

• 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; and they have the team leadership and coding skills (e.g. Python, Java, and Scala) to build and operate in production; and to help ensure performance, security, scalability, and robust data integration.

• →Design and 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
• →Learn and understand customer technology environments and systems
• →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
• →Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models
• →Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle
• →Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment
• →Ensure the quality of the delivered product

• 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

• A Master’s or other advanced degree in data science or a related field
• Hands-on experience with one or more ecosystem technologies (e.g., Spark, Databricks, Snowflake, AWS/Azure/GCP)
• Relevant side projects (e.g. contributions to an open source technology stack)
• Experience working with Data-Science and Machine-Learning software and libraries such as h2o, TensorFlow, Keras, scikit-learn, etc. 
• Experience with Docker, Kubernetes, or some other containerization technology
• AWS Sagemaker (or Azure ML) and MLflow experience
• Experience building enterprise ML models