AWS ML Engineer - Only W2 candidates

Please refer to the position below and let me know if you have any suitable consultant for the same.

Job Title - ML Engineer
Location: Hybrid 3 times a week in Mclean, VA or NYC, NY
Type W2 Role
Please Note - This Position only for W2 candidates

• 1st References Name-
  Title-
  Company -
  Email (Official email)
  Contact -
  2nd References - Name-
  Title-
  Company -
  Email (Official email)
  Contact -

One and Done Interview, coding and behavioral
Must haves:
Technical must haves:

Python

AWS (Solutions Architect level knowledge - ECS, EC2, etc.)

Kubernetes

Kubeflow (nice to have)

Spark

Pandas

NumPy

PySpark

Technical nice to haves:

Data analysis experience (SQL)

ML tooling: mlplot; Databricks

AWS solution Architect Cert

Job Description:
Tech Requirements/Must haves:

• Python
• AWS (Solutions Architect level knowledge - ECS, EC2, etc.)
• Kubernetes
• Machine Learning practices (databricks, etc) - Train and Deploy ML models
• Spark

PlGood to have:

• Data analysis experience (SQL)
• ML tooling: mlplot; Databricks
• AWS solution Architect Cert
• Kubeflow
• Pandas
• NumPy
• PySpark
• Build ML models

LOB: Card Tech - Machine Learning

Groups: Small Business card, Acquisitions, and Partnerships

Team: BCP - Business Consumer Products

What they will be doing:

• Supporting discover integration across all groups and enterprise
• Train and deploy machine learning models
• Work closely with data scientists
• Support models for:
• Credit card decisioning
• Fraud tracking
• Risk assessment
• Partner applications (Kohl's, BJs)
• Kubeflow Usage: leverage kubeflow in 2 distinct areas, build and train, and serving. For build and train we focus on designing new standardized pipelines to enable our DS partners to complete R&D on their model and to iterate on the final feature set and model object. We leverage preexisting pipelines for serving our batch models, real time serving is done via API on a different platform. All of this is done on the cloud via AWS.