Azure Consultant

Your responsibilities:

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• Collaborate with data scientists/forecaster to deploy machine learning models into production environments.
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• Follow deployment strategies in place to ensure safe and controlled rollouts.
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• Design and manage the infrastructure required for hosting ML models, including Azure cloud resources.
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• Utilize containerization technologies like Docker to package models and dependencies.
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• Establish Azure monitoring solutions to track the performance and health of deployed models. Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
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• Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.
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• Optimize ML infrastructure for scalability and cost-effectiveness.
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• Implement auto-scaling mechanisms to handle varying workloads efficiently such as parallel run
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• Enforce security best practices to safeguard both the models and the data they process.
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• Ensure compliance with industry regulations and data protection standards.
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• Oversee the management of data pipelines and data storage systems required for model training and inference.
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• Implement data versioning and lineage tracking to maintain data integrity.
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• Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints.
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• Collaborate with DevOps teams to align MLOps practices with broader organizational goals.
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• Continuously optimize and fine-tune ML models for better performance.
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• Identify and address bottlenecks in the system to enhance overall efficiency.
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• Maintain comprehensive documentation for deployment processes, configurations, and system architecture.
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Communicate effectively with non-technical stakeholders, providing insights into the performance and impact of ML models

Desirable skills/knowledge/experience:

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• 5+ years of experience in MLOps, DevOps or a related field.
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• Strong understanding of machine learning principles and model lifecycle management.
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• Passionate about making things work iteratively and automating + scaling them
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• Deep knowledge of software development and engineering in combination with ML models
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• Experience in development Azure Machine Learning or any MLOPs frameworks
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• Experience with SQL and noSQL environments, Azure SQL database and Storage Account blob is must
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• Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
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• Experience with cloud platforms Azure machine learning services.
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• Experience with monitoring tools and practices for model performance in production.
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• practical ability in creating build and release pipelines in Azure DevOps for ML artifacts
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• experience in supporting real-time-inference scenarios with Azure Machine Learning
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• Knowledge of tools, methods, and frameworks used by data scientists
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• Familiarity with data engineering practices and tools.
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• Familiarity with data formats such as GRIP, NETCDF, Parquet, and JSON is a plus.
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• Azure data scientist associate certificate is plus
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