MLOps Architect

overall Experience of 1215 Yrs Experience supporting machine learning projects.

Expert with ML platforms (e.g. TensorFlow PyTorch).

Experience with cloud platforms (e.g. Azure).

Bachelors degree in Computer Science Engineering or a related field.

Proven experience as a Data Engineer or in a similar role.

Experience with big data tools (e.g. Hadoop Spark) and databases (e.g. SQL NoSQL).

Knowledge of machine learning concepts and workflows.

Strong programming skills (e.g. Python Java).

Excellent problemsolving abilities and attention to detail.

Strong communication skills to effectively collaborate with other teams.

• Design and optimize pipelines for model deployments in production environments using containers (Docker or Azure Kubernetes) Azure DevOps and/or MLOps and Azure Data Factory.
• Knowledge of Azure Databricks pipeline and ML Model deployment is mandatory.
• Implement efficient microservices frameworks i.e. API Management message broker load balancing etc
• Troubleshoot improve and scale continuous integration continuous delivery and continuous deployment (CI/CD) pipelines
• Write design documents to build consensus for new systems components and enhancements to existing components
• Extensive programming experience in Python and/or R with knowledge of ObjectOriented Programming.
• Experience in Azure DevOps & Azure Cloud Services (e.g. Azure Blob Azure Key Vault Azure Data Factory) or similar experience with AWS or Google
• Experience with CI/CD pipelines Automated Testing Automated Deployments Agile methodologies Unit Testing and Integration Testing tools
• Demonstrated history of designing solution pipelines from conception to deployment in production environments e.g. Docker containers on Kubernetesbased platforms with data orchestration in Azure Data Factory

Nice to Have:

• Knowledge of JFrog Artifactory is a plus.
• Experience with frontend user interface development using various HTMLrelated tool frameworks like Django FastAPI (Python) Java/Javascript etc
• Experience with in memory and/or distributed computing frameworks (e.g. Spark Hadoop)
• Conduct performance testing on API endpoints and batch jobs to identify and correct CPU and memory bottlenecks

Skill

Mandatory

Proficiency Level (15)

Azure DevOps CI/CD

Y

45

Azure Cloud Engineer

Y

4 5

Databricks Pipelines

Y

34

Familiarity with ML platforms (e.g. TensorFlow PyTorch) & MLOPS

Y

3

Azure ML

N

34

Knowledge of JFrog Artifactory

N

23 (Good to have)