Senior AWS Full Stack ML Engineer
About The Position
CGI has an immediate need for a Senior AWS Full Stack ML 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 AWS Full Stack ML Engineer to operationalize and optimize large-scale financial modeling platforms. This role blends machine learning expertise, full-stack software engineering, and deep AWS cloud knowledge, with a strong emphasis on building scalable and secure MLOps ecosystems. The ideal candidate will bridge the gap between data science experimentation and enterprise-grade production systems. You will transform model prototypes into resilient, compliant, and high-performance solutions while ensuring reliability, observability, and cost efficiency in a regulated financial environment. This position requires hands-on ownership of ML pipelines, infrastructure automation, cloud architecture, and cross-functional collaboration to deliver secure and scalable AI-driven applications.
Requirements
- 6+ years of experience in MLOps, DevOps, or ML engineering
- Proven experience designing and maintaining end-to-end MLOps pipelines
- Hands-on experience with CI/CD tools (GitLab, Jenkins, AWS CodePipeline)
- Experience with workflow orchestration tools such as AWS Step Functions or Apache Airflow
- Strong proficiency in Python
- Experience with ML frameworks such as: TensorFlow PyTorch Scikit-learn
- Ability to refactor and productionize data science code
- Solid understanding of model performance monitoring and data drift detection
- Deep experience with AWS services, including: Amazon SageMaker S3, EC2 EKS / ECS / Fargate Lambda AWS Glue IAM
- Experience implementing Infrastructure as Code using Terraform or AWS CloudFormation
- Strong knowledge of secure AWS architectures (IAM roles, VPC configurations, encryption best practices)
- Experience optimizing AWS environments for performance, availability, and cost efficiency
- Experience designing scalable ETL pipelines and data workflows
- Ensuring data quality and availability for training and inference at scale
- Monitoring and alerting using tools such as Amazon CloudWatch
- Strong understanding of microservices architecture and system design
- Familiarity with containerization (Docker) and orchestration (ECS/EKS)
- Experience with Git-based version control and code quality practices
- Knowledge of testing frameworks and debugging methodologies
- Strong problem-solving and troubleshooting skills
- Ability to work cross-functionally with data scientists, engineers, and business stakeholders
- Clear communicator who can translate complex ML infrastructure topics into practical solutions
Nice To Haves
- Experience in financial modeling environments or regulated industries
- AWS Certified Machine Learning – Specialty
- AWS Certified Solutions Architect – Associate
- Other relevant cloud or ML certifications
Benefits
- Competitive compensation
- Comprehensive insurance options
- Matching contributions through the 401(k) plan and the share purchase plan
- Paid time off for vacation, holidays, and sick time
- Paid parental leave
- Learning opportunities and tuition assistance
- Wellness and Well-being programs
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
