Data Engineer - GenAI

CGI•Pittsburgh, PA

15h•Onsite

About The Position

This role will require someone at our client site 5 days a week in either Pittsburgh, PA, Cleveland, OH, Birmingham, AL, or Dallas, TX. In this role, we are seeking a highly skilled and innovative Azure Generative AI Expert to design, develop, and implement cutting-edge AI solutions using Microsoft Azure's AI capabilities. The ideal candidate will leverage Azure OpenAI Service, Cognitive Services, and related tools to build robust AI models and solutions tailored to our business needs. This role demands a deep understanding of generative AI, prompt engineering, and cloud computing within the Azure ecosystem.

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.
Understanding of agentic frameworks (such as AutoGen or LangGraph).
ML: Familiar with CI/CD Pipeline, Jenkin, Model Training, Model Deployment.
GenAI: Knowledge in LLM Models, Langchain , Hugingface.
Excellent organizational and analytical abilities.
Outstanding problem solver.
Good written and verbal communication skills.

Responsibilities

Design, develop, and deploy generative AI solutions leveraging Azure OpenAI Service and Azure Cognitive Services.
Create and optimize large language models (LLMs) for various use cases such as text generation, summarization, and content personalization.
Develop robust and scalable pipelines for data preprocessing, model training, and deployment.
Examine new data system requirements and implement migration models.
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.