CloudIngest

Lead Data Engineer + AI / Machine Learning

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Lead Data Engineer specializing in AI/Machine Learning, with a 6-month contract at $65/hr in Alpharetta, GA/NJ. Key skills include AWS, Python, data pipelines, and ML workflows; hybrid engineering experience is essential.
🌎 - Country
United States
πŸ’± - Currency
$ USD
-
πŸ’° - Day rate
520
-
πŸ—“οΈ - Date
July 2, 2026
πŸ•’ - Duration
Unknown
-
🏝️ - Location
On-site
-
πŸ“„ - Contract
Unknown
-
πŸ”’ - Security
Unknown
-
πŸ“ - Location detailed
Alpharetta, GA
-
🧠 - Skills detailed
#Data Engineering #"ETL (Extract #Transform #Load)" #Snowflake #Scala #SageMaker #AWS SageMaker #Deployment #Data Pipeline #Datasets #ML (Machine Learning) #Data Science #S3 (Amazon Simple Storage Service) #AWS (Amazon Web Services) #AI (Artificial Intelligence) #AWS S3 (Amazon Simple Storage Service) #Monitoring #Python
Role description
Data Engineer + AI / machine learning Onsite -Alpharetta GA / NJ Rate-65 /hr on c2c 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: β€’ Build scalable data pipelines and data models β€’ Work hands-on with Python β€’ Develop or integrate machine learning models and inference workflows β€’ Contribute to MLOps pipelines (deployment, monitoring, lifecycle) Our environment is AWS-centric, and relevant experience is important, particularly: β€’ AWS (S3, Glue, SageMaker, ECS/Fargate) β€’ Working with data platforms like Snowflake β€’ Building data pipelines and ML workflows end-to-end The team will be working on use cases such as: β€’ Building merchant-level analytical datasets and feature pipelines β€’ Performing feature engineering and model-ready dataset creation β€’ Developing recommendation systems (e.g., nearest neighbor, ML-based models) β€’ Supporting model training, evaluation, and inference pipelines in AWS (SageMaker/ECS) At a high level: β€’ The Engineers will execute across data pipelines, feature engineering, and ML integration β€’ 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 β€œnice-to-have”, not a requirementβ€”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