Data Engineer with ML & AWS

FUSTIS LLC

Contract principal Hybrid

Reston, Virginia, US$70 – $75Posted February 26, 2026

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PythonSQLAWSGitSparkAirflow

Job Description

Job Title- Data/Modelling Engineer

Location: Hybrid Reston, Virginia(3 days onsite in every week)-Need Local Only

Visa: USC/GC/GC-EAD Only for W2 & C2C open only for H1B visa not other

Mode of Interview: In-Person Interview

15 years exp.

Need One Manager reference not colleague or Lead with LinkedIn URL

Overview

We are seeking a highly skilled ML/Data Engineer to lead model development, experiment tracking, and end to end machine learning operations across Domino and Amazon SageMaker. This role will drive model lifecycle quality, governance alignment, and engineering excellence.

Responsibilities

Own the monitoring, tracking, and maintenance of ML models across Domino and SageMaker platforms.
Implement MLflow for parameters, metrics, artifact management, and end to end lineage.
Build and maintain scalable data pipelines for training, validation, and inference processes.
Develop custom evaluation metrics, explainability components, and fairness/bias testing frameworks.
Package models for deployment and support model lifecycle transitions across environments.
Collaborate with data scientists, engineering teams, and governance stakeholders to ensure compliance and operational readiness.

Required Skills & Experience

Strong experience with AWS and ML engineering
Proficiency in Python and MLflow
Hands on expertise with Domino and SageMaker SDKs
Experience with feature engineering and scalable data pipelines
Knowledge of model validation, explainability, and bias/fairness tooling
Familiarity with Git based workflows, version control, and MLOps practices

Focused on manipulating data in a software engineering capacity. Some of that data might live in relational systems, but it s increasingly moving towards NoSQL systems and data lakes.

Normalize databases and ascertain the structure of the data meets the requirements of the applications that are accessing the information. Construct datasets that are easy to analyze and support company requirements.

Combine raw information from different sources to create consistent and machine-readable formats.