Machine Learning Engineer with AWS

Role :

ML Data Engineer Location :

Berkeley heights NJ and Alpharetta GA , This team will be working on a data + machine learning platform focused on building recommendation systems and advanced analytics using large-scale merchant datasets. The goal is not to hire traditional ETL-focused engineers or pure data scientists. Instead, we re targeting hybrid Data Engineers who can: Build scalable data pipelines and data models Work hands-on with Python Develop or integrate machine learning models and inference workflows Contribute to MLOps pipelines (deployment, monitoring, lifecycle) Our environment is AWS-centric, and relevant experience is important, particularly: AWS (S3, Glue, SageMaker, ECS/Fargate) Working with data platforms like Snowflake Building data pipelines and ML workflows end-to-end The team will be working on use cases such as: Building merchant-level analytical datasets and feature pipelines Performing feature engineering and model-ready dataset creation Developing recommendation systems (e.g., nearest neighbor, ML-based models) Supporting model training, evaluation, and inference pipelines in AWS (SageMaker/ECS) At a high level: The Engineers will execute across data pipelines, feature engineering, and ML integration Within the pod, we expect a mix of strengths (some stronger in ML, others in core data engineering) We ve also included Agentic/LLM-based experience as a nice-to-have , not a requirement this helps future-proof the team without narrowing the candidate pool too much.

Key profile we re targeting:

Data Engineers who can move beyond pipelines and help build systems that generate insights and drive recommendations from data