Senior AI/ML Engineer

CGI•Reston, VA

22h•$108,300 - $137,100•Hybrid

About The Position

CGI has an immediate need for a Senior AI/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 skilled AI/ML Engineer to design, build, and deploy end-to-end machine learning and Generative AI solutions in a cloud-native AWS environment. This role will focus on developing scalable AI systems, operationalizing advanced models, and building production-ready microservices that power intelligent applications. The ideal candidate is comfortable working across the full lifecycle of AI/ML development—from experimentation and model training to deployment, monitoring, and optimization. You will play a key role in architecting Retrieval-Augmented Generation (RAG) systems, implementing MLOps best practices, and delivering high-performance, scalable solutions that drive real business value. This is a hands-on technical role suited for someone who enjoys solving complex problems, building reliable systems, and working at the intersection of AI innovation and cloud engineering.

Requirements

5+ years of proficiency in Python for AI/ML development and backend engineering
Experience designing and deploying end-to-end machine learning pipelines
2+ years of hands-on experience with Generative AI and Large Language Models (LLMs)
Practical experience building and operationalizing RAG pipelines, embeddings, and vector databases
Expertise in AWS cloud services, including: Lambda ECS/Fargate S3 API Gateway DynamoDB RDS/Aurora SageMaker Bedrock
Experience developing microservices using frameworks such as FastAPI or Flask
Familiarity with building scalable data pipelines and real-time inference systems
Strong understanding of MLOps principles, including: Model versioning Containerized training and inference Automated retraining Monitoring and performance optimization
Experience implementing CI/CD pipelines (GitHub, GitLab, or AWS CodePipeline)
Experience with Infrastructure as Code (Terraform or CloudFormation)
Solid understanding of distributed systems, cloud architecture, and API design
Ability to work independently while collaborating effectively with cross-functional teams

Responsibilities

design, build, and deploy end-to-end machine learning and Generative AI solutions in a cloud-native AWS environment
developing scalable AI systems
operationalizing advanced models
building production-ready microservices that power intelligent applications
architecting Retrieval-Augmented Generation (RAG) systems
implementing MLOps best practices
delivering high-performance, scalable solutions that drive real business value