ML Data Engineer Systems Analyst

Reston, VA - USA

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

(Local candidates only as F2F Interview is must)

Project Overview

Client is seeking an experienced ML/Data Engineer to support the development deployment and lifecycle management of machine learning models. This role focuses on building scalable ML infrastructure implementing model tracking and governance and supporting end-to-end machine learning operations using Domino and Amazon SageMaker. The position works closely with data scientists engineering teams and governance stakeholders to ensure model reliability compliance and operational readiness.

Key Responsibilities

Monitor track and maintain machine learning models across Domino and Amazon SageMaker platforms.
Implement MLflow for parameter tracking metrics management artifact storage and full model lineage.
Design and maintain scalable data pipelines to support training validation and inference processes.
Develop custom evaluation metrics explainability components and fairness/bias testing frameworks.
Package machine learning models for deployment and manage model lifecycle transitions across environments.
Collaborate with data scientists engineering teams and governance stakeholders to maintain compliance and operational standards.

Required Qualifications

Bachelors degree in Computer Science Information Systems or a related field.
Post-graduate degree preferred.
Professional certifications are desirable.
15 years of relevant professional experience in data engineering machine learning engineering or related roles.

Technical & Soft Skills

Technical Skills

Strong experience working with AWS and machine learning engineering environments.
Proficiency in Python and MLflow for experiment tracking and model management.
Hands-on experience with Domino and Amazon SageMaker SDKs.
Experience with feature engineering and building scalable data pipelines.
Knowledge of model validation explainability techniques and bias/fairness testing tools.
Familiarity with Git-based workflows version control systems and MLOps practices.

Soft Skills

Strong collaboration skills when working with cross-functional teams.
Ability to maintain governance and compliance standards across machine learning workflows.
Strong analytical and problem-solving abilities.

(Local candidates only as F2F Interview is must) Project Overview Client is seeking an experienced ML/Data Engineer to support the development deployment and lifecycle management of machine learning models. This role focuses on building scalable ML infrastructure implementing model tracking and gove...