ML Engineer

• Ensure that Health and Safety is the number one goal by following policies, processes, and always acting in a safe manner.

• Implement, configure, and manage various Microsoft Azure solutions and services: You will be responsible for implementing and managing a variety of Azure solutions and services, including but not limited to Azure Machine Learning, Azure Databricks, and Azure Data Factory.

• Collaborate with IT teams and business units to design Azure architectures: You will work closely with other IT teams and business units to design and implement Azure architectures that meet the needs of the organization.

• Assist in the migration of data, and services to the Azure cloud platform: You will play a key role in migrating existing data, and services to the Azure cloud platform and integration to both Cloud and OnPrem Applications.

• Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale: You will be responsible for designing and implementing data pipelines and engineering infrastructure that can support machine learning systems at an enterprise scale.

• Take offline models data scientists build and turn them into a real machine learning production system: You will work with data scientists to take their offline models and turn them into production-ready machine learning systems.

• Develop and deploy scalable tools and services for handling machine learning training and inference: You will develop and deploy tools and services that can handle machine learning training and inference at scale.

Qualifications

• A master’s degree in computer science, Statistics, Mathematics, or a related field is required. A bachelor’s degree with significant relevant experience would suffice.

• 6+ years working in the IT industry with 4+ years of experience in data science, machine learning, or a related field. 

• Strong understanding of machine learning techniques and algorithms: You should have a strong understanding of various machine learning techniques and algorithms, including supervised and unsupervised learning, as well as deep learning.

• Experience with Microsoft Azure’s data services: You should have hands-on experience with various data services offered by Microsoft Azure, such as Azure Machine Learning, Azure Databricks, and Azure Data Factory.

• Familiarity with Azure Cognitive Services for integrating AI capabilities (like vision, language, and decision functionalities) directly into applications.

•  Ability to work with large datasets and experience with data processing tools like SQL, Pandas, and big data technologies such as Apache Spark.

•  Familiarity with version control systems like Git and development environments like Jupyter notebooks or VS Code and Azure DevOps (CI/CD) - MLOps process

• Experience with data pipelines and engineering infrastructure: You should have experience designing and implementing data pipelines and engineering infrastructure for machine learning systems.

• Deep Understanding of statistical models, machine learning algorithms, and big data technologies. Proven experience in deploying machine learning models into production.

• Proficiency in Python, .NET C#, API development 

• Experience with SQL, NoSQL databases and Data Fabric architectures.

• Experience with machine learning frameworks like PyTorch, Tensorflow or Scikit-learn preferred.

• Knowledge of GenAI preferred.

• Strong communication skills to effectively collaborate with team members and stakeholders.

• Strong problem-solving skills and ability to think algorithmically.

• Ability to translate complex findings into a compelling narrative for non-technical stakeholders.

  • Certifications like Microsoft Certified: Azure Data Scientist Associate or Microsoft Certified: Azure AI Engineer Associate will be preferred.