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# MLOps Engineer with Azure Experience

- **Company:** Everest Technologies, Inc.
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Microsoft Azure, Information Systems, Computer Programming, DevOps, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Tensorflow, Azure Machine Learning, Sentiment Analysis, Chatbots, Pytorch, Containerization, Scikit Learn, Kubernetes, Deployment Automation, Machine Learning Operations, Software Library, Docker
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/14d2df22-7329-445d-9b19-a60cc68d483f

## About the Role

* Bachelor's Degree, (BA/BS) in Information Systems from a four-year college or university and 5 or more years of development experience required or equivalent combination or education and experience

 * Total of 3-6 years of experience in managing machine learning projects end-to-end, with the last 18 months focused on MLOps

 * Strong programming skills, preferably in languages like Python, Java, or Scala

 * Proficiency in machine learning libraries and frameworks, such as TensorFlow, PyTorch, or scikit-learn

 * Experience with containerization technologies, like Docker and Kubernetes

 * Familiarity with ML model deployment tools, such as MLflow or Kubeflow

 * Working experience in Azure cloud platform

## Description

* Utilize Azure technologies like Azure Cognitive Technologies, Azure Machine Learning, and Azure Bot Services to design, create, and deploy AI/ML based applications.

 * Include AI components into data workflows, engage with data scientists and data engineers.

 * Utilize Azure AI services to implement natural language processing (NLP) create and implement machine learning models and algorithms.

 * Automate the deployment and monitoring of AI models, collaborate with DevOps teams.

 * Use AI to automate processes such as sentiment analysis, image identification, recommendation systems, and chatbots.

 * Implementing machine learning pipelines and workflows

 * Deploying and scaling ML models in production environments

 * Automating CI/CD pipelines to account for data, code, and model changes

 * Monitoring model performance and applying updates as needed

 * Ensuring the security and compliance of machine learning systems

 * Collaborating with data scientists to optimize models and improve performance


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