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# Data Machine Learning Engineer

- **Company:** TekLeaders, Inc
- **Location:** McLean, VA, United States
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Data Analysis, Continuous Integration, Software Debugging, DevOps, Python (Programming Language), Machine Learning, NumPy, Software Deployment, SQL Databases, Workflow Management Systems, Feature Engineering, Apache Spark, Pandas, Kubernetes, Machine Learning Operations, Jenkins, Databricks
- **Published:** August 27, 2026
- **Apply:** https://www.dice.com/job-detail/8fb8a226-023b-4139-8b4d-93117db6564f

## About the Role

Python

 - AWS

 - Kubernetes

 - Kubeflow (or equivalent workflow experience)

 - Spark pandas, NumPy

 - ML Ops / ML tooling experience

 - Hybrid on-site requirement (must be able to work in-office; McLean preferred, New York possible)

 - Previous Capital One experience highly desirable

Nice to haves:

 - SQL / data analysis experience

 - Databricks

 - Additional ML tooling experience (mlplot, Data bricks)

 - DevOps familiarity (Jenkins, CICD pipelines)

 - AWS solution Architect Cert, * Experience with MLOps and ML tooling.
 * Proficiency in Python.
 * Knowledge of Kubernetes and AWS.
 * Experience with Kubeflow or equivalent workflow tools.
 * Familiarity with Spark, pandas, and NumPy., * Previous experience with the client is desirable.
 * Experience with SQL and data analysis.
 * Familiarity with Databricks.
 * Knowledge of additional ML tooling, such as mlplot.
 * Understanding of DevOps concepts, including Jenkins and CI/CD pipelines.
 * An AWS Solution Architect Certification is considered an asset.

## Description

* Maintain and develop ML serving pipelines using Kubeflow, Spark, and Python.
 * Collaborate with Data Science teams on training pipelines and feature engineering.
 * Develop features, deploy applications, perform testing, and implement vulnerability fixes.
 * Debug and provide support for production ML pipelines and CI/CD workflows.
 * Support integration efforts across various groups and the enterprise.
 * Build, train, and deploy machine learning models.
 * Support models related to credit card decisioning, fraud tracking, and risk assessment.


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