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 Dev Ops 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, Dev Ops 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 Tensor Flow, 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 Dev Ops 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