ML Platform Engineer - AWS/SageMaker & MLOps

TMV Global Inc; Atlanta (GA); On-site; USD 150,000 - 190,000; Full time

Job summary

TMV Global Inc. is seeking an ML Engineer to own model monitoring, validation, and lifecycle across Domino and SageMaker. Build scalable data pipelines for training and inference, implement MLflow for metrics and artifacts, and ensure reproducibility across environments.

You will collaborate with data scientists and engineers to apply feature engineering, explainability, and bias testing while ensuring deployment readiness across on-site locations in Atlanta, GA and Reston, VA.

Qualifications

• Strong AWS and ML engineering experience.
• Proficient in Python and MLflow.
• Hands-on with Domino and SageMaker SDKs.
• Experience building scalable data pipelines and feature engineering.
• Knowledge of model validation, explainability and bias tooling.
• Familiar with Git workflows and MLOps practices.
• Experience with SQL, data modeling, Spark/Hive/Airflow.

Responsibilities

• Own monitoring, tracking, and maintenance of ML models across Domino and SageMaker.
• Implement MLflow for parameters, metrics, artifacts, and end-to-end lineage.
• Build and maintain scalable data pipelines for training, validation, and inference.
• Develop evaluation metrics, explainability components, and fairness testing frameworks.
• Package models for deployment and support lifecycle transitions across environments.
• Collaborate with data scientists, engineering teams, and governance stakeholders.

Skills

AWS

ML engineering

Python

MLflow

Git workflows

MLOps

Data pipelines

Feature engineering

Model validation

Explainability

Bias testing

SQL

Spark/Hive/Airflow

NoSQL

Education

Bachelor's degree in Computer Science, Information Systems or related field

Tools

Domino

SageMaker SDKs

MLflow

SQL

Spark

Hive

Airflow

Git

Job description

TMV Global Inc. is seeking an ML Engineer to own model monitoring, validation, and lifecycle across Domino and SageMaker. Build scalable data pipelines for training and inference, implement MLflow for metrics and artifacts, and ensure reproducibility across environments.

You will collaborate with data scientists and engineers to apply feature engineering, explainability, and bias testing while ensuring deployment readiness across on-site locations in Atlanta, GA and Reston, VA.