AI/ML Engineer

CGI•Reston, VA

1d•Hybrid

About The Position

We're looking for an AI/ML Engineer who enjoys building practical, scalable AI solutions and working closely with business teams to bring ideas to life. In this role, you'll design and deploy machine learning models—especially those involving large language models (LLMs) and generative AI—while leveraging AWS cloud services. You'll play a key part in shaping intelligent systems, from early-stage experimentation and prompt engineering to production deployment and performance optimization. The ideal candidate is comfortable working across the full ML lifecycle and stays current with evolving AI 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 either Reston, VA. A hybrid working model is acceptable.

Requirements

4+ years of relevant experience in machine learning, data science, or AI engineering
Solid experience building and deploying machine learning models in real-world environments
Hands-on work with large language models (OpenAI, Anthropic, Cohere, etc.) and prompt engineering techniques
Strong Python skills, along with familiarity with libraries like TensorFlow, PyTorch, or scikit-learn
Comfortable working within AWS—particularly services like Bedrock, S3, EC2, Lambda, and SageMaker
Experience setting up data pipelines and APIs to support ML workflows
Good understanding of MLOps practices, including model deployment, monitoring, and iteration
Ability to translate business needs into technical solutions without overcomplicating things
Familiarity with data analysis tools such as Pandas, R, or similar
Exposure to building or deploying AI agents or generative AI-based solutions
Strong problem-solving mindset and the ability to explain technical ideas clearly

Nice To Haves

Experience fine-tuning or customizing foundation models
Working knowledge of Docker or container orchestration tools
Awareness of data privacy and security considerations in cloud environments

Responsibilities

Design and deploy machine learning models, especially those involving large language models (LLMs) and generative AI, while leveraging AWS cloud services.
Shape intelligent systems, from early-stage experimentation and prompt engineering to production deployment and performance optimization.
Work across the full ML lifecycle and stay current with evolving AI technologies.