

CloudIngest
Lead Data Engineer + AI / Machine Learning
β - 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.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
520
-
ποΈ - Date
July 2, 2026
π - Duration
Unknown
-
ποΈ - Location
On-site
-
π - Contract
Unknown
-
π - Security
Unknown
-
π - Location detailed
Alpharetta, GA
-
π§ - 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, weβre 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)
Weβve also included Agentic/LLM-based experience as a βnice-to-haveβ, not a requirementβthis helps future-proof the team without narrowing the candidate pool too much.
Key profile weβre targeting: Data Engineers who can move beyond pipelines and help build systems that generate insights and drive recommendations from data
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, weβre 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)
Weβve also included Agentic/LLM-based experience as a βnice-to-haveβ, not a requirementβthis helps future-proof the team without narrowing the candidate pool too much.
Key profile weβre targeting: Data Engineers who can move beyond pipelines and help build systems that generate insights and drive recommendations from data





