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# AI/ML Engineer

- **Company:** Raas Infotek LLC
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Computer Programming, Continuous Integration, Machine Learning, Natural Language Processing, Tensorflow, Data Processing, Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Generative AI, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Azure AI, Restful APIs, Docker, Databricks
- **Published:** November 3, 2026
- **Apply:** https://www.dice.com/job-detail/cfd68a7d-62be-4891-bcb8-ee64edd1dd33

## About the Role

* Strong experience in Artificial Intelligence and Machine Learning.
 * Proficiency in Python programming.
 * Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
 * Strong knowledge of machine learning algorithms and deep learning.
 * Experience with Generative AI, LLMs, NLP, and prompt engineering.
 * Knowledge of RAG (Retrieval-Augmented Generation) and vector databases.
 * Experience with data processing, model training, and evaluation.
 * Knowledge of AWS, Azure, or Google Cloud Platform.
 * Experience with Docker, Kubernetes, and MLOps.
 * Knowledge of REST APIs, CI/CD, and model deployment., * Bachelor's degree in Computer Science, Data Science, AI/ML, or a related field.
 * Master's degree is preferred., * AWS Certified Machine Learning Engineer - Associate.
 * Google Cloud Professional Machine Learning Engineer.
 * Microsoft Certified: Azure AI Engineer Associate.
 * Databricks Certified Machine Learning Professional.
 * Relevant AI/ML or Generative AI certifications are preferred.

## Description

* Design, develop, and deploy AI/ML models.
 * Build and optimize machine learning algorithms.
 * Develop Generative AI and LLM-based applications.
 * Implement RAG pipelines and AI-powered solutions.
 * Analyze data and improve model performance.
 * Deploy and maintain production-ready AI/ML applications.
 * Monitor models and troubleshoot technical issues.
 * Collaborate with data scientists, developers, and business teams.