TKWW is looking for a Senior ML Engineer to help grow all the MLOps capabilities inside the company building state-of-the-art machine learning infrastructure. This person will be part of the MLOps team, collaborating closely with data scientists to develop tools that will speed up their work and continuously improve the deployment of ML models into production. In addition, this role will also include working with the latest LLMs to provide state-of-the-art solutions to the company. As an ML Engineer you will be working in a fast-paced environment and using cutting-edge technologies to develop a scalable MLOps platform that will support years of company growth and build a best-in-class web product for couples.

Responsibilities:

Be an essential part of the MLOps team.  Work with data science and business stakeholders to deploy scalable machine learning models in production in a timely manner.  Assemble large, complex data sets that meet requirements to support data science and analytics projects.  Contribute to expanding and improving 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, and implement internal process improvements including automating manual work, optimizing data delivery, re-designing infrastructure for greater scalability.   Support daily operations and production of our data platform, including monitoring, quality, and governance utilities, CI/CD pipelines, and all data integration touchpoints.  Apply the latest AI technologies to TKWW's suite of customer products offerings.

Requirements:

Bachelor’s degree in Computer Science, Engineering or related field. Excellent communication skills in English (oral and written).  3+ years of engineering experience with 1+ years of experience building big data pipelines using Python, SQL, and Apache Spark.  3+ years deploying and maintaining ML models in production with demonstrable business impact. Strong understanding of cloud architecture tools and services, such as AWS and cloud data warehouses. Working knowledge of open-source data orchestration tools such as Apache Airflow and experience applying them.  Experience working with vector databases is a plus (Qdrant, ChromaDB…). Willingness to learn new technologies and approaches to solve new problems. Snowflake experience is a plus. Experience on working with LLMs will also be an advantage.