AI/ML Engineer

CGI•Reston, VA

4d•$89,600 - $211,600•Hybrid

About The Position

We are seeking a skilled AI/ML Engineer to design, build, and deploy scalable machine learning solutions. In this role, you will work with large datasets to develop and optimize models, leveraging modern frameworks and cloud 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. You'll be responsible for building end-to-end AI pipelines—from data ingestion and processing to model deployment—while ensuring performance, reliability, and scalability. The role requires close collaboration with both technical and business teams to identify opportunities where AI can drive meaningful impact. This is a hands-on position suited for someone who enjoys solving complex problems, working with cutting-edge tools, and deploying production-ready AI solutions in cloud environments.

Requirements

Solid experience building and tuning machine learning or deep learning models (TensorFlow, PyTorch, or similar)
6+ yearsa of programming background, particularly in Python (R or Java is a plus)
Experience developing and maintaining end-to-end ML pipelines
Comfort working with cloud platforms like AWS for model deployment
Good understanding of CI/CD processes, especially in the context of AI/ML workflows
Ability to work with complex datasets and extract meaningful insights
Strong problem-solving mindset with the ability to translate business needs into technical solutions
Familiarity with collaborative, cross-functional environments

Nice To Haves

Exposure to MLOps tools such as MLflow or Kubeflow
Experience with big data technologies (Spark, Hadoop, Databricks)
Knowledge of advanced AI domains like NLP or computer vision
Relevant certifications in AI/ML or cloud platforms

Responsibilities

building end-to-end AI pipelines—from data ingestion and processing to model deployment—while ensuring performance, reliability, and scalability
close collaboration with both technical and business teams to identify opportunities where AI can drive meaningful impact
solving complex problems
working with cutting-edge tools
deploying production-ready AI solutions in cloud environments