Senior ML/Data Engineer

CGI•Reston, VA

3h•$107,700 - $154,300•Hybrid

About The Position

CGI has an immediate need for a Senior ML/Data Engineer to join our team. This is an exciting opportunity to work in a fast-paced team environment supporting one of the largest customers. We take an innovative approach to supporting our client, working side-by-side in an agile environment using emerging technologies. We partner with 15 of the top 20 banks globally, and our top 10 banking clients have worked with us for an average of 26 years!. This role is located at a client site in Reston, VA. A hybrid working model is acceptable. We are seeking a highly experienced ML/Data Engineer to lead end-to-end machine learning operations, model lifecycle management, and data engineering initiatives across Domino and Amazon SageMaker platforms. This role is responsible for ensuring model quality, governance alignment, operational stability, and scalable data infrastructure. You will oversee model monitoring, experiment tracking, and lifecycle transitions while building robust data pipelines to support training, validation, and inference workloads. The position requires strong collaboration with data scientists, platform engineers, and governance teams to maintain compliance, explainability, and production readiness across enterprise ML environments. This is a hybrid role based in Reston, requiring three days onsite and two days remote per week.

Requirements

7+ years hands-on experience with AWS and ML engineering practices
Proficiency in Python and deep experience implementing MLflow for experiment tracking and lineage
Practical expertise with Domino and Amazon SageMaker SDKs
Experience building and maintaining scalable data pipelines for training and inference
Strong background in feature engineering and dataset preparation
Knowledge of model validation, explainability techniques, and fairness/bias evaluation frameworks
Experience packaging and deploying models across development, test, and production environments
Solid understanding of Git-based workflows, version control, and MLOps best practices
Strong SQL and data modeling skills
Experience working with relational databases, NoSQL platforms, and data lakes
Familiarity with tools such as Spark, Hive, and Airflow
Ability to design structured, analysis-ready datasets from diverse data sources

Responsibilities

lead end-to-end machine learning operations
model lifecycle management
data engineering initiatives across Domino and Amazon SageMaker platforms
ensuring model quality, governance alignment, operational stability, and scalable data infrastructure
oversee model monitoring, experiment tracking, and lifecycle transitions
building robust data pipelines to support training, validation, and inference workloads
maintain compliance, explainability, and production readiness across enterprise ML environments