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 responsibilities1.AI, ML, data, and enterprise agent operations supportProvide operational support for AI, ML, data products, and enterprise agents across development, non-production, and production environmentsMonitor pipelines, services, workflows, applications, and deployed agents for failures, degradation, abnormal behaviour, or support issuesSupport incident triage, investigation, root-cause analysis, and resolution coordinationWork with delivery teams, business owners, and the Center of Practice to improve operational stability and reduce recurring issuesEnsure production support expectations are clear before AI products or agents are released to users2.Enterprise agent request management and operational coordinationSupport the intake, tracking, triage, and operational coordination of enterprise agent requests submitted by business teamsMaintain visibility into the agent request pipeline, including request status, priority, dependencies, risks, target users, and expected support modelCoordinate with AI Product Owners, Business Partners, the Center of Practice, Security, Data Governance, and Platform teams to ensure requests follow the approved operating modelValidate 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 requirementsHelp distinguish requests requiring simple Copilot enablement, Copilot Studio configuration, workflow automation, or more advanced Azure/Foundry engineering supportEnsure agent requests are not treated as isolated builds, but as governed digital products with clear lifecycle ownership and operational accountability3.MLOps, DataOps, DevOps, and AgentOps practicesSupport establishing and executing MLOps, AIOps, DataOps, DevOps, and AgentOps practices aligned with enterprise standardsSupport model, data pipeline, and agent deployment, versioning, rollback, lifecycle management, and controlled promotion across environmentsEnable repeatable, auditable deployment and operational patterns for AI products and agentsReduce reliance on manual, fragile, or non-scalable operational processesMaintain operational runbooks, support checklists,



and release-readiness artifacts for recurring AI and agent deployment patterns4.Logging, monitoring, observability, and agent telemetryOwn 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 applicableMaintain log tables, metadata, and telemetry to support traceability, diagnostics, auditability, and continuous improvementImplement monitoring dashboards and alerting for system health, reliability, performance, adoption, agent usage, failure rates, and escalation patternsEnsure logs and telemetry align with enterprise standards, privacy expectations, access controls, and governance requirementsProvide operational visibility into which agents are being used, by whom, for what purpose, and whether they are delivering expected value within approved boundaries5.Model, data, agent, and quality assurance supportMonitor AI model performance, stability, drift, and quality indicators in productionSupport validation and quality checks for models, data pipelines, workflows, prompts, knowledge sources, and deployed agentsEnsure testing, security, access, and quality gates are enforced before production releasesPartner with AI developers, data scientists, business analysts, and the Center of Practice to improve robustness, reliability, and user experienceHelp identify recurring agent failures, weak knowledge grounding, missing permissions, misunderstood user intents, broken automations, or poor escalation paths6.Post-deployment agent support and continuous improvementProvide first-line or coordinated second-line operational support for deployed enterprise agents, depending on the agreed support modelTriage user-reported issues such as access problems, broken workflows, unexpected answers, missing knowledge sources, performance degradation, or failed handoffsCoordinate fixes with the appropriate owner, including the Center of Practice, AI developers, business owners, platform teams, data owners, or vendorsMaintain support documentation, known-error logs, FAQs, release notes, and operational runbooks for deployed agentsAnalyze post-deployment feedback and usage telemetry to recommend improvements, retirement, consolidation, retraining, or escalation of agentsEnsure agents remain current when source documents, business processes, policies, integrations, or access models change7.Product analytics and usage telemetryImplement product analytics and usage telemetry for AI solutions and enterprise agents, including usage patterns, adoption signals, performance metrics, user satisfaction indicators, operationalindicators, cost indicators, and support demandProvide insights to Product Owners, business owners, Center of Practice leads, and stakeholders to support value tracking and optimisationEnable transparency into how AI products and agents are used and perform over timeIdentify low-adoption, high-risk, duplicative, or high-support-demand agents that require intervention8.Cloud architecture, governance, and compliance alignmentEnsure AI, data, and enterprise agent solutions align with enterprise cloud architecture standardsWork closely with Cloud Architecture, Infrastructure, Security, Governance, Data, and Center ofPractice teamsSupport compliance with security, access control, data-handling, responsible AI, and approved-source policiesProvide operational evidence for governance reviews, audits, and agent portfolio reviewsHelp ensure deployed agents remain within approved security, data access, privacy, retention, and auditability boundaries9.Cost, capacity, and resource monitoringMonitor cloud resource utilisation across AI, data,



and agent workloadsSupport cost optimisation and capacity-planning effortsProvide visibility into trade-offs between performance, scalability, reliability, user adoption, and costFlag inefficiencies, abnormal consumption, duplicated agents, or support risks proactively10.Release readiness and production enablementSupport production readiness and go-live reviews for AI products, data products, and enterprise agentsEnsure operational requirements are met prior to wider deployment, including monitoring, ownership, support path, access controls, release notes, rollback approach, and user communicationEnable smooth handover from development into ongoing supportImprove confidence in scaling AI solutions and enterprise agents beyond pilot stageConfirm that each deployed agent has a business owner, technical owner, support model, telemetry baseline, and lifecycle review cadenceQualificationsEducationBachelor's degree in computer science, engineering, information systems, data analytics, or a related fieldEquivalent practical experience is acceptableExperience5+ years of experience in platform operations, DevOps, DataOps, MLOps, cloud operations, enterprise application support, or AI operations rolesExperience supporting production analytics, data, AI, automation, or enterprise agent systemsExperience working with enterprise IT, cloud, security, governance, or platform teamsExperience with Microsoft Azure and Azure-based servicesExperience supporting analytics and AI workloads using platforms such as Microsoft Fabric or equivalentExperience supporting Microsoft 365, Copilot, Copilot Studio, Power Platform, Azure AI, or equivalent enterprise agent and automation platforms is an assetExperience coordinating support queues, request intake, production incidents, release readiness, or service management processes is an assetTechnical knowledgeStrong understanding of MLOps and model lifecycle managementStrong understanding of data pipelines and data quality monitoringStrong understanding of DevOps, CI/CD, release management, and production support practicesFamiliarity with logging, monitoring, observability tooling, metadata management, telemetry, and product analyticsFamiliarity with enterprise agent patterns, prompt/knowledge-source governance, access controls, workflow automation, and post-deployment agent supportFamiliarity with governance and security patterns in enterprise cloud environmentsSkillsStrong diagnostic and problem-solving skillsAbility to operate across technical depth, service management discipline, and enterprise constraintsClear communication with both technical and non-technical stakeholdersStrong documentation and operational disciplineAbility to coordinate requests across business teams, Center of Practice resources, platform teams, security, governance, and vendorsAbility to translate user issues into clear operational tickets, root-cause themes, improvement actions, and release-readiness requirementsPersonal attributesDetail-oriented and pragmaticStructured and reliable under operational pressureStrong ownership and accountability mindsetCollaborative and service-orientedComfortable supporting business users while maintaining disciplined governance and operational controlsContinuous-improvement mindset focused on making AI and agent operations repeatable, scalable, and safe.Success measuresSuccess in this role may be measured through:Operational stability and reliability of AI products, data products, and enterprise agentsQuality and consistency of logging, telemetry, monitoring, and support documentationClear visibility and disciplined tracking of enterprise agent requests from intake to deployment and supportReduction in operational incidents, recurring agent issues, and manual interventionsImproved visibility into usage, adoption, performance, cost, and support demandImproved speed and quality of post-deployment support for enterprise agentsIncreased confidence in scaling AI solutions and enterprise agents into productionEvidence that deployed agents remain governed, supportable, current, and aligned with approved data sources and enterprise standards
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📌 Ai Platform Operations Support (Medellín)
🏢 Kruger
📍 Medellín