Job Summary : Data Machine Learning Engineer (MLOps)
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Role: MLOps/Data Machine Learning Engineer
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Location: Hybrid (McLean, VA preferred; some team members in New York)
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Client: Capital One
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Pay Rate: $70/hr W2
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Eligibility: US Citizens and Green Card holders only
Core Responsibilities:
- Develop and maintain machine learning (ML) serving pipelines using Kubeflow, Spark, and Python.
- Collaborate with Data Science teams on training pipelines and feature engineering.
- Build, train, and deploy ML models for applications such as credit card decisioning, fraud tracking, and risk assessment.
- Deploy applications, develop new features, perform testing, and address vulnerabilities.
- Debug and support production ML pipelines and CI/CD workflows.
- Support integration efforts across multiple enterprise groups.
- Work on partner applications (e.g., Kohl's, BJs).
Required Skills & Qualifications:
- Strong experience in MLOps and ML tooling.
- Proficiency in Python programming.
- Hands-on experience with AWS and Kubernetes.
- Experience with Kubeflow or comparable ML workflow tools.
- Strong knowledge of Spark, pandas, and NumPy.
- Ability to work in a hybrid, on-site environment (McLean, VA preferred).
Preferred Skills:
- Previous experience working with Capital One.
- SQL and data analysis expertise.
- Experience with Databricks and additional ML tools (e.g., mlplot).
- Familiarity with DevOps practices, Jenkins, and CI/CD pipelines.
- AWS Solution Architect Certification.
Team/Project Details:
- Join a team of 6 Data Engineers within the Card Tech Machine Learning (CTML) group.
- Work primarily on ML serving pipelines and collaborate closely with Data Science teams.
Interview Process:
- 1st round: 30-minute job-fit interview.
- 2nd round: 1-hour technical coding assessment (for selected candidates).
or continue with email
-
Role: MLOps/Data Machine Learning Engineer
-
Location: Hybrid (McLean, VA preferred; some team members in New York)
-
Client: Capital One
-
Pay Rate: $70/hr W2
-
Eligibility: US Citizens and Green Card holders only
Core Responsibilities:
- Develop and maintain machine learning (ML) serving pipelines using Kubeflow, Spark, and Python.
- Collaborate with Data Science teams on training pipelines and feature engineering.
- Build, train, and deploy ML models for applications such as credit card decisioning, fraud tracking, and risk assessment.
- Deploy applications, develop new features, perform testing, and address vulnerabilities.
- Debug and support production ML pipelines and CI/CD workflows.
- Support integration efforts across multiple enterprise groups.
- Work on partner applications (e.g., Kohl's, BJs).
Required Skills & Qualifications:
- Strong experience in MLOps and ML tooling.
- Proficiency in Python programming.
- Hands-on experience with AWS and Kubernetes.
- Experience with Kubeflow or comparable ML workflow tools.
- Strong knowledge of Spark, pandas, and NumPy.
- Ability to work in a hybrid, on-site environment (McLean, VA preferred).
Preferred Skills:
- Previous experience working with Capital One.
- SQL and data analysis expertise.
- Experience with Databricks and additional ML tools (e.g., mlplot).
- Familiarity with DevOps practices, Jenkins, and CI/CD pipelines.
- AWS Solution Architect Certification.
Team/Project Details:
- Join a team of 6 Data Engineers within the Card Tech Machine Learning (CTML) group.
- Work primarily on ML serving pipelines and collaborate closely with Data Science teams.
Interview Process:
- 1st round: 30-minute job-fit interview.
- 2nd round: 1-hour technical coding assessment (for selected candidates).
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