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# Senior AI/ML Engineer

- **Company:** Raas Infotek LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Microsoft Azure, Cloud Engineering, Continuous Integration, Text Processing, Python (Programming Language), Machine Learning, Natural Language Processing, Performance Tuning, OpenAI, Tensorflow, Standard Sql, Enterprise Software Applications, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Deep Learning, Generative AI, Agentic-AI, Fastapi, Scikit Learn, HuggingFace, Machine Learning Operations, Restful APIs, Data Pipelines
- **Published:** November 5, 2026
- **Apply:** https://www.dice.com/job-detail/9d456b0c-bfb9-4156-b74f-a73750ed45ae

## About the Role

* Python, SQL
 * Machine Learning and Deep Learning
 * TensorFlow / PyTorch / Scikit-learn
 * Natural Language Processing (NLP)
 * Generative AI and Large Language Models (LLMs)
 * Retrieval-Augmented Generation (RAG)
 * LangChain / LangGraph
 * OpenAI / Azure OpenAI / Hugging Face
 * REST APIs / FastAPI / Flask
 * MLOps and Model Deployment

## Description

We are looking for an experienced Senior AI/ML Engineer with 12+ years of experience in designing, developing, and deploying advanced Artificial Intelligence and Machine Learning solutions. The ideal candidate should have strong expertise in Python, Machine Learning, Deep Learning, Generative AI, LLMs, and cloud-based AI platforms., * Design, develop, and deploy scalable AI/ML models and applications.

 * Build machine learning algorithms for classification, regression, clustering, and predictive analytics.
 * Develop Generative AI solutions using LLMs, RAG, and Agentic AI architectures.
 * Implement NLP solutions, text processing, and conversational AI applications.
 * Build and optimize data pipelines for model training and inference.
 * Develop REST APIs and integrate AI/ML models with enterprise applications.
 * Implement model monitoring, evaluation, and performance optimization.
 * Work with cross-functional teams to understand business requirements.
 * Implement MLOps practices for model deployment, CI/CD, and lifecycle management.
 * Ensure AI solutions meet security, scalability, and performance requirements.