Senior ML/Data Engineer (15+ Years Of EXP)\\ Local to VA ONLY

Senior ML/Data Engineer (15+ Years Of EXP)

Reston, VA

12+ Months

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

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 degree in Computer Science, Information Systems or related field

2) Post-graduate degree desired

3) Professional certification(s) desired 15+ years relevant experience

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.