Azure Consultant

Your responsibilities:

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

Communicate effectively with non-technical stakeholders, providing insights into the performance and impact of ML models

Desirable skills/knowledge/experience:

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

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