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# GenAI Engineer

- **Company:** Capgemini
- **Location:** New Brunswick, United States
- **Experience:** Experienced
- **Salary:** $90,000.0 - $110,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Computer Programming, Information Engineering, Python (Programming Language), Tensorflow, Azure Machine Learning, Data Processing, Pytorch, Delivery Pipeline, Large Language Models, Prompt Engineering, Generative AI, Scikit Learn, Information Technology, Xgboost, Databricks
- **Published:** August 17, 2026
- **Apply:** https://www.dice.com/job-detail/f315ee16-def5-4497-bfb0-b892b5615d94

## About the Role

* Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
 * Minimum 3 years of professional experience and at least 2 years of experience in AI/ML development and deployment.
 * Proven experience designing and implementing Generative AI solutions using Large Language Models (LLMs) and modern GenAI frameworks.
 * Hands-on experience with Azure Databricks for data processing, model development, and operationalization.
 * Strong programming skills in Python.
 * Experience with machine learning frameworks such as Scikit-learn, PyTorch, TensorFlow and XGBoost

## Description

* Design, develop, and deploy Generative AI solutions supporting HR use cases
 * Develop scalable data science and machine learning pipelines using Azure Machine Learning and Azure Databricks
 * Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, and AI agents.
 * Collaborate with Business Analysts, HR stakeholders, Product Owners, and Solution Architects to translate business challenges into measurable AI and GenAI solutions.
 * Develop and operationalize AI models with a strong focus on reproducibility, explainability, scalability, and performance.
 * Utilize Azure DevOps, Azure ML, and Databricks to automate AI development and deployment workflows.
 * Optimize training and inference workloads for performance, scalability, and cost efficiency within Azure cloud environments.
 * Partner closely with Data Engineering teams to ensure high-quality, reliable, and feature-ready datasets for AI and GenAI applications .


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