Senior Machine Learning Engineer

About the role

Job Description:

Data Machine Learning Engineer

Mclean, VA - Hybrid

long term contract only on w2

Must haves:

• 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

Org/Team:

• (CTML) Card Tech Machine Learning
• Team works on serving pipelines and collaborates with Data Science teams
• Team locations: primarily McLean (majority) and New York (some members)
• Joining a team of 6 Data Engineers

Project Details/Day2Day:

• Maintain and develop ML serving pipelines (Kubeflow + Spark + Python)
• Work with DS teams on training pipelines and feature engineering
• Develop features, deploy applications, test, and perform vulnerability fixes
• Debugging and supporting production ML pipelines and CICD workflows
• Supporting discover integration across all groups and enterprise
• Build, train, and deploy machine learning models
• Support models for:

o Credit card decisioning

o Fraud tracking

o Risk assessment

o Partner applications (Kohl's, BJs)

IV Process:

• Round 1: 30-minute job-fit interview
• Round 2: 1-hour technical coding assessment interview (for candidates who pass job-fit)

Role Overview

We are seeking an MLOps Engineer to join a team focused on machine learning technology. This role involves the maintenance and development of ML serving pipelines. The engineer will collaborate with Data Science teams on various projects, including training pipelines and feature engineering. The position requires a hybrid on-site presence, with a preference for McLean, VA,

Key Responsibilities

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.

Required Qualifications

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.

Preferred Qualifications

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.