The AI Platform Operations Support role is responsible for enabling the reliable, observable, secure, and governed operation of AI, machine learning, data products, and enterprise agents across the organisation.

This role supports AI, Machine Learning, Data, and business-led agent delivery teams by implementing and operating MLOps, DataOps, DevOps, and AgentOps practices, along with comprehensive logging, monitoring, telemetry, product analytics, request tracking, and post-deployment support processes. The role ensures that AI solutions and enterprise agents are production-ready, operationally stable, cost-aware, supportable, and aligned with enterprise cloud architecture, security, and governance standards.

The role acts as a key bridge between AI/Data delivery teams, the Center of Practice, business requestors, IT Infrastructure, Cloud Architecture, Security, Governance, and Platform teams, ensuring AI products and enterprise agents scale safely and sustainably beyond pilots.

This is not a product ownership role and not a pure infrastructure role. It is an operational enablement and support role focused on observability, request intake coordination, production support, quality, governance alignment, and operational sustainability.

The purpose of this role is to ensure that AI products, data products, and enterprise agents are supported by strong operational foundations rather than ad-hoc or fragile production setups.

This includes supporting the operational flow of enterprise agent requests from intake through deployment and ongoing support; owning operational logging, usage telemetry, metadata management, monitoring, analytics, and quality controls across AI pipelines, platforms,



and deployed agents; and ensuring alignment with enterprise cloud architecture, governance policies, access controls, and cost-management practices.

Key responsibilities

1.

- AI, ML, data, and enterprise agent operations support
- Provide operational support for AI, ML, data products, and enterprise agents across development, non-production, and production environments
- Monitor pipelines, services, workflows, applications, and deployed agents for failures, degradation, abnormal behaviour, or support issues
- Support incident triage, investigation, root-cause analysis, and resolution coordination
- Work with delivery teams, business owners, and the Center of Practice to improve operational stability and reduce recurring issues
- Ensure production support expectations are clear before AI products or agents are released to users

2.

- Enterprise agent request management and operational coordination
- Support the intake, tracking, triage, and operational coordination of enterprise agent requests submitted by business teams
- Maintain visibility into the agent request pipeline, including request status, priority, dependencies, risks, target users, and expected support model
- Coordinate with AI Product Owners, Business Partners, the Center of Practice, Security, Data Governance, and Platform teams to ensure requests follow the approved operating model




- Validate that required information is captured before build or deployment, including business owner, intended users, approved data sources, access model, risk level, expected value, and support requirements
- Help distinguish requests requiring simple Copilot enablement, Copilot Studio configuration, workflow automation, or more advanced Azure/Foundry engineering support
- Ensure agent requests are not treated as isolated builds, but as governed digital products with clear lifecycle ownership and operational accountability

3.

- MLOps, DataOps, DevOps, and AgentOps practices
- Support establishing and executing MLOps, AIOps, DataOps, DevOps, and AgentOps practices aligned with enterprise standards
- Support model, data pipeline, and agent deployment, versioning, rollback, lifecycle management, and controlled promotion across environments
- Enable repeatable, auditable deployment and operational patterns for AI products and agents
- Reduce reliance on manual, fragile, or non-scalable operational processes
- Maintain operational runbooks, support checklists, and release-readiness artifacts for recurring AI and agent deployment patterns

4.

- Logging, monitoring, observability, and agent telemetry
- Own operational logging across AI, data, and agent platforms, including pipeline execution logs, application and system logs, model inference and performance logs, agent conversation and action telemetry, data quality and validation logs, and support ticket or request history where applicable
- Maintain log tables, metadata, and telemetry to support traceability, diagnostics, auditability, and continuous improvement
- Implement monitoring dashboards and alerting for

📌 AI Platform Operations Support (Medellín)
🏢 Kruger
📍 Medellín