What You Will Do
Design, develop, and maintain high-performance, scalable MLOps systems and pipelines that interact with AI models and systems.Build and operate end-to-end ML workflows on Azure Machine Learning, including training pipelines, experiment tracking, model registry, and managed online/batch endpoints.
Cooperate with cross-functional teams, including data scientists, AI researchers, and AI/ML engineers, to understand requirements, define project scope, and ensure alignment with business goals.Contribute technical expertise in evaluating, selecting, and implementing software technologies, tools, and frameworks in a cloud-native (Azure + Azure ML) environment.
Troubleshoot and resolve intricate software problems, ensuring optimal performance and reliability when interfacing with AI/ML systems.
Participate in software development project planning and estimation, ensuring efficient resource allocation and timely solution delivery.
Contribute to the development of continuous integration and continuous deployment (CI/CD) pipelines.
Contribute to the development of high-performance data pipelines, storage systems, and data processing solutions.
Implement monitoring and observability for deployed models and platform services (performance, data/model drift, reliability), with dashboards and reporting in Power BI and Grafana.
Contribute to the creation and maintenance of technical documentation, including design specifications, API documentation, data models, data flow diagrams, and user manuals. Mentor junior engineers and promote MLOps and software engineering best practices across the team.
What You Will Need
Minimum Qualifications
Bachelor's degree or equivalent in Computer Science, Engineering, or a related field.7-10 years of relatable work experience, including a minimum of 4 years in machine learning operations or software/platform development.
Strong knowledge of machine learning concepts, technologies, and integration techniques.
Strong knowledge of programming languages commonly used in AI/ML, such as Python, R, or C++.
Hands-on experience with Azure Machine Learning (workspaces, pipelines, compute, model registry, and endpoints) and the broader Azure cloud platform.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras, and model/experiment tracking tools such as MLflow.
Experience with Agile methodologies (Scrum, Kanban) and project management tools such as Jira or Azure DevOps.Experience with version control systems, such as Git, and CI/CD tools, such as Jenkins, GitLab CI/CD, or Azure DevOps.
Knowledge of containerization technologies like Docker and Kubernetes, and infrastructure-as-code tools such as Terraform or Azure Resource Manager (ARM) templates.Knowledge of data modeling techniques, and familiarity with data pipeline orchestration tools.
Experience building monitoring and visualization dashboards with Power BI and Grafana.
Travel Percentage: None
