ML Ops Services

Recruiter:

Recru-it

Job Ref:

PE011627/LM

Date posted:

Friday, June 12, 2026

Location:

South Africa, South Africa

Salary:

Market Related Monthly

SUMMARY:
Qualifications:

3-year diploma or higher in the related field of study, for example Computer Science or Information Technology.

Environment and Dependencies:

Azure Databricks
Azure DevOps (ADO)
Microsoft Teams

Key deliverables:

Monitoring dashboards (live and production-ready)
Alerting frameworks and triggers
Data quality gate definitions and implementation
CI/CD architecture and pipeline designs
ML project templates and reusable components
Operational runbooks and documentation

Scope of Work:
Monitoring and observability:

Dashboarding for model health and pipeline execution.
Drift detection and anomaly monitoring.
Automated alerting (email/dashboards).

Data quality and feature pipelines:

Data quality rules (schema, null checks, thresholds).
Feature pipeline readiness standards.
Data contracts and SLA definitions.

CI/CD and DevOps Integration:

CI/CD design for batch ML workloads including but not limited to explicitly enforced testing gates.
Deployment automation and rollback strategies.
Integration with Azure DevOps / Git repositories.

Reusable frameworks and templates:

ML project templates (cookie-cutter) and development frameworks including model development tracking, experiment tracking, model registry, model monitoring etc.
Standardized pipeline components for data transformation and validation.
Scalable and standardized logging.
Documentation and reproducibility frameworks.

Governance and operations:

Runbooks for production support.
PR review processes and governance.
Knowledge transfer to internal teams.
Compliance with secure data handling processes and privacy controls.

POSITION INFO:
Qualifications:

3-year diploma or higher in the related field of study, for example Computer Science or Information Technology.

Environment and Dependencies:

Azure Databricks
Azure DevOps (ADO)
Microsoft Teams

Key deliverables:

Monitoring dashboards (live and production-ready)
Alerting frameworks and triggers
Data quality gate definitions and implementation
CI/CD architecture and pipeline designs
ML project templates and reusable components
Operational runbooks and documentation

Scope of Work:
Monitoring and observability:

Dashboarding for model health and pipeline execution.
Drift detection and anomaly monitoring.
Automated alerting (email/dashboards).

Data quality and feature pipelines:

Data quality rules (schema, null checks, thresholds).
Feature pipeline readiness standards.
Data contracts and SLA definitions.

CI/CD and DevOps Integration:

CI/CD design for batch ML workloads including but not limited to explicitly enforced testing gates.
Deployment automation and rollback strategies.
Integration with Azure DevOps / Git repositories.

Reusable frameworks and templates:

ML project templates (cookie-cutter) and development frameworks including model development tracking, experiment tracking, model registry, model monitoring etc.
Standardized pipeline components for data transformation and validation.
Scalable and standardized logging.
Documentation and reproducibility frameworks.

Governance and operations:

Runbooks for production support.
PR review processes and governance.
Knowledge transfer to internal teams.
Compliance with secure data handling processes and privacy controls.

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