

CloudIngest
Data Engineer Lead with Gen AI (Only W2) (Expect H1B))
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer Lead with Gen AI, a 12-month contract based in Berkeley Heights, NJ, or Alpharetta, GA. Key skills include AWS, data pipelines, and machine learning integration. Local candidates only; W2 employment required.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
-
ποΈ - Date
July 2, 2026
π - Duration
More than 6 months
-
ποΈ - Location
On-site
-
π - Contract
W2 Contractor
-
π - Security
Unknown
-
π - Location detailed
Berkeley Heights, NJ
-
π§ - Skills detailed
#Data Engineering #"ETL (Extract #Transform #Load)" #Scala #AWS (Amazon Web Services) #SageMaker #AWS SageMaker #Deployment #Data Pipeline #Datasets #ML (Machine Learning) #Data Science #S3 (Amazon Simple Storage Service) #AI (Artificial Intelligence) #Cloud #AWS S3 (Amazon Simple Storage Service) #Monitoring
Role description
NOTE: Need only W2 (Any Visa Except H1B)
Please share relevant profiles to Ranjit@cloudingest.com
Job Role/Title: Data engineer Lead with Gen AI
Contract- 12 Months
Location: Berkeley heights NJ or Alpharetta GA-(Only Local)
Job Descriptio
β’ nThis team will be working on a data + machine learning platform focused on building recommendation systems and advanced analytics using large-scale merchant datasets
β’ .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 model
β’ sWork hands-on with Pytho
β’ nDevelop or integrate machine learning models and inference workflow
β’ sContribute to MLOps pipelines (deployment, monitoring, lifecycle
β’ )Our environment is AWS-centric, and relevant experience is important, particularly
β’ :AWS (S3, Glue, SageMaker, ECS/Fargate
β’ )Working with data platforms like Snowflak
β’ eBuilding data pipelines and ML workflows end-to-en
β’ dThe team will be working on use cases such as
β’ :Building merchant-level analytical datasets and feature pipeline
β’ sPerforming feature engineering and model-ready dataset creatio
β’ nDeveloping 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 integratio
β’ nWithin the pod, we expect a mix of strengths (some stronger in ML, others in core data engineering
β’ )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
β’ .Key profile weβre targeting
β’ :Data Engineers who can move beyond pipelines and help build systems that generate insights and drive recommendations from dat
a
NOTE: Need only W2 (Any Visa Except H1B)
Please share relevant profiles to Ranjit@cloudingest.com
Job Role/Title: Data engineer Lead with Gen AI
Contract- 12 Months
Location: Berkeley heights NJ or Alpharetta GA-(Only Local)
Job Descriptio
β’ nThis team will be working on a data + machine learning platform focused on building recommendation systems and advanced analytics using large-scale merchant datasets
β’ .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 model
β’ sWork hands-on with Pytho
β’ nDevelop or integrate machine learning models and inference workflow
β’ sContribute to MLOps pipelines (deployment, monitoring, lifecycle
β’ )Our environment is AWS-centric, and relevant experience is important, particularly
β’ :AWS (S3, Glue, SageMaker, ECS/Fargate
β’ )Working with data platforms like Snowflak
β’ eBuilding data pipelines and ML workflows end-to-en
β’ dThe team will be working on use cases such as
β’ :Building merchant-level analytical datasets and feature pipeline
β’ sPerforming feature engineering and model-ready dataset creatio
β’ nDeveloping 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 integratio
β’ nWithin the pod, we expect a mix of strengths (some stronger in ML, others in core data engineering
β’ )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
β’ .Key profile weβre targeting
β’ :Data Engineers who can move beyond pipelines and help build systems that generate insights and drive recommendations from dat
a






