Senior Machine Learning/GenAI Architect

CGI•Pittsburgh, PA

1d•Onsite

About The Position

CGI is looking to hire a highly skilled and innovative ML/AI Architect with deep traditional AI/ML expertise, including model development, data science, MLOps, and model validation, along with exposure to GenAI and agentic AI patterns such as RAG, orchestration, tool integration, and governed workflow automation. 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.

Requirements

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 of data architecture in the 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.
Understanding of agentic frameworks (such as AutoGen or LangGraph, MCP).
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

Deep traditional AI/ML expertise, including model development, data science, MLOps, and model validation.
Exposure to GenAI and agentic AI patterns such as RAG, orchestration, tool integration, and governed workflow automation.
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.
Excellent Communication and Leadership qualities to work independently with minimal supervision.

Responsibilities

Maintain existing predictive models while architecting for the future.
Architect applications that integrate predictive models and generative AI.
Build and deploy predictive models based on Random Forest, Linear Regression etc. for problems such as fraud detection, anomaly detection, churn prediction etc. at enterprise scale.
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.