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Fractal Analytics is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite empowers imagination with intelligence. And that it will be such Fractalites that will continue to build the company for the next 100 years.
Please visit Fractal | Intelligence for Imagination for more information about Fractal.
Role Overview
As a key member of Fractal’s dynamic team, this role focuses on implementing cutting-edge MLOps practices, primarily using Azure cloud technologies, to operationalize machine learning models. The candidate will collaborate with diverse cross-functional teams to automate and streamline the ML lifecycle, ensuring robust deployment, scalability, and continuous improvement of ML models. This position requires a blend of expertise in cloud technologies, programming skills, and a commitment to innovation and continuous learning in the evolving landscape of machine learning operations.
Responsibilities
Implementing MLOps Practices: Design and implement robust MLOps solutions integrating continuous integration, continuous deployment (CI/CD), and continuous training (CI/CT) of machine learning models, particularly using Azure DevOps and other relevant tools.
Model Development and Deployment: Collaborate with data scientists and ML engineers to bring machine learning models to operational status, ensuring their deployment in production environments with a focus on scalability and performance.
Automation: Streamline the machine learning lifecycle by automating processes like data collection, model training, testing, deployment, and monitoring, utilizing Azure services (e.g., Azure Machine Learning, Azure Databricks, Azure Pipelines).
Monitoring and Maintenance: Set up and manage monitoring systems for ML models in production to track their performance, accuracy, and drift, and address any emerging issues by updating the models as needed.
Collaboration and Communication: Work closely with cross-functional teams, including data scientists, software engineers, and IT operations, to ensure a seamless integration of machine learning workflows.
Documentation and Reporting: Maintain thorough documentation of MLOps processes and provide regular reports on the status and performance of machine learning models to relevant stakeholders.
Qualifications
Demonstrate proficiency in Azure cloud services, with a particular emphasis on those related to machine learning (e.g., Azure Machine Learning, Azure Databricks), and a solid understanding of cloud architecture principles.
Possess strong programming skills in languages like Python and experience in scripting for automation purposes.
Experience with version control systems such as Git and familiarity in managing and collaborating on code.
Ensure that machine learning solutions adhere to security policies and data privacy regulations.
Actively seek opportunities to improve machine learning workflows, enhance efficiency, reduce costs, and address complex technical challenges.
Stay informed about the latest trends and developments in MLOps, machine learning, and Azure cloud technologies.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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