AI/ML Engineer

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

Location -Atlanta, GA and Reston, VA (Onsite)

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 its 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. Skills:
• This IT role requires a significant set of technical skills, including a deep knowledge of SQL, data modeling, and tools like Spark/Hive/Airflow.

Education/Work Exprerience:

1) bachelor's degree in computer science, Information Systems or related field