Machine Learning Engineer

Tekfortune Inc.; North Dakota; Hybrid; USD 120,000 - 170,000; Full time

Job summary

Tekfortune Inc. in McLean, VA seeks a Machine Learning/MLOps Engineer to maintain and develop ML serving pipelines using Kubeflow, Spark, and Python. The role collaborates with Data Science teams on training pipelines, feature engineering, deployment, and CI/CD.

Hybrid on-site requirement; McLean preferred, New York possible; prior Capital One experience desirable; join a team of ML engineers focused on serving pipelines and enterprise integration.

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.
• Hybrid on-site presence; McLean preferred (New York possible).
• Previous Capital One experience highly desirable

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, test, and perform vulnerability fixes.
• Debug and support production ML pipelines and CI/CD workflows.
• Support integration across groups and enterprise.
• Build, train, and deploy machine learning models.
• Support models for credit card decisioning, fraud tracking, and risk assessment.

Skills

Python

AWS

Kubernetes

Kubeflow

Spark

pandas

NumPy

ML Ops tooling

Tools

Kubeflow

Jenkins

CI/CD pipelines

Databricks

mlplot

Job description

++Machine Learning Engineer++

Hybrid in Mclean, VA
Contract

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.