MLOps Engineer with Azure Experience

ESSENTIAL DUTIES:

•                    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

POSITION REQUIREMENTS & COMPETENCIES:

•                    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

•                    Automation of machine learning model deployment