ABOUT THE ROLE AND OUR TEAM:  

TKWW is looking for an MLOps engineering manager that will architect and build a state-of-the-art machine learning infrastructure for the company. This person will lead a small team of talented MLOps engineers and collaborate closely with data scientists to develop tools that will speed up their work and convert model prototypes into production ML systems. In addition, this role will lead the development of batch and real-time data pipelines as well as assemble large datasets to support a variety of ML and data science use cases. As an MLOps engineering manager, you will be working in a fast-paced environment and using cutting-edge cloud technologies to develop a scalable data platform that will support years of company growth. 

RESPONSIBILITIES

• Grow, lead, and mentor a team of MLOps engineers. 
• Work with data science and business stakeholders to deploy scalable machine learning models in production in a timely manner. 
• Design and implement a self-service platform to speed up development and automate the deployment and scaling of ML models in production. 
• Assemble large, complex data sets that meet requirements to support data science and analytics projects. 
• Responsible for architecting and managing the infrastructure to support all stages of the machine learning model lifecycle, including feature engineering, feature store, model training, testing, monitoring, and deployment in a production environment.
• Proactively identify, design, and implement internal process improvements including automating manual work, optimizing data delivery, re-designing infrastructure for greater scalability. 
• Build applications using open-source frameworks for big data processing and data orchestration (e.g. Apache Airflow, Kubernetes, Spark). 
• Support daily operations and production of our data platform, including monitoring, quality, and governance utilities, CI/CD pipelines, and all data integration touchpoints. 
  

SUCCESSFUL CANDIDATE HAVE:

• Bachelor’s degree in Computer Science, Engineering, Data Science or related field.
• Excellent communication skills in English (oral and written). 
• 5+ years of data platform / engineering experience with 3+ years of experience building big data pipelines using Python, SQL, and Apache Spark. 
• 2+ years deploying and maintaining ML models in production with demonstrable business impact. 
• Strong understanding of cloud architecture tools and services, such as S3, EMR, Kubernetes, Lambda functions, and cloud data warehouses (prior AWS experience is highly desirable).
• Experience building streaming pipelines using Kafka and Spark or similar technologies.
• Working knowledge of open-source data orchestration tools such as Apache Airflow, Luigi, Dagster, or Prefect and experience applying them. 
• Snowflake experience is a plus.

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