Senior GenAI Engineer

CGI•Dallas, TX

18h•Onsite

About The Position

This role will require someone at our client site 5 days a week in Pittsburgh, PA, Cleveland, OH, or Dallas, TX. For this role on this particular client engagement, employer sponsorship of immigration related visa and/or green card status as part of the PERM process will not be available. CGI is looking to hire highly skilled and innovative GenAI Engineer with exposure to GenAI and agentic AI patterns such as RAG, orchestration, tool integration, and governed workflow automation.

Requirements

Bachelor's degree in computer science, computer engineering, or relevant field.
A minimum of 6 + years experience as a Data Engineer or similar role with 1-2 years specifically focused on GenAI or AI/ML projects.
Strong knowledge data architecture in banking domain.
4+ Years Work Experience in Python, Mongo.
Strong hands-on experience with Microsoft Azure.
Integrate LLMs with enterprise datasets using Azure Open AI.
ML: Familiar with CI/CD Pipeline, Jenkins, Model Training, Model Deployment.
GenAI: Knowledge in LLM Models, Langchain, Hugging Face.
Excellent organizational and analytical abilities.
Outstanding problem solver.
Good written and verbal communication skills.

Nice To Haves

Strong experience in building and deploying predictive models based on Random Forest, Linear Regression etc. for problems such as fraud detection, anomaly detection, churn prediction etc. at enterprise scale.
Strong programming skills in Python and in similar languages.
Familiarity with machine learning frameworks like PyTorch.
Hands-on experience with generative AI models such as GPT.
Understanding of MLOps practices for building scalable AI pipelines.
Solid understanding of natural language processing (NLP) and AI ethics.
Understanding of agentic frameworks (such as AutoGen or LangGraph, MCP).

Responsibilities

Maintain existing predictive models while architecting for the future.
Architect application that works as an integration of predictive model and generative AI.
Develop robust and scalable pipelines for data preprocessing, model training, and deployment.
Translate complex requirements into scalable and efficient architecture.
Design and implement scalable ML pipelines with CI/CD automation, efficient model training, validation, and deployment across enterprise environments.