Middle Strong/Senior ML Data Engineer

Experience designing and implementing large scale cost effective production workflows within AWS (lambda, kafka, kinesis, EKS, step functions). Experience working with data scientists to transition notebooks (Databricks, Sagemaker) into production code. Cost management experience. Proficiency in Python. DevOps experience (CloudWatch, CI/CD pipelines, IaC). Pipeline Orchestration experience, ideally Airflow. MLFlow experience (nice to have). DBT experience (nice to have). Frontend Experience (nice to have). Spark / Pyspark experience (nice to have).

Work with data scientists to design scalable ML pipelines that integrate into existing architecture. Take ownership of all ML ops and data engineering deliverables for an assigned project. Support the deployment of models into production environments (batch and/or real-time), including containerization, inference orchestration, and integration with relevant tools and services. Build internal tools (e.g., Streamlit apps) to showcase ML outputs. Display continual curiosity to drive innovation. Collaborate with platform teams to adopt and extend data ingestion framework. Adopt and contribute to company wide best practices around coding quality, architecture, and code reviews.