At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal. Job Description Responsible for operationalizing AI at scale to improve network performance, service reliability, and operational efficiency. They bridge data science, network engineering, and operations, ensuring AI models move from experimentation into real-time, mission-critical use.DUTIES & RESPONSIBILITIES1. AI Operationalization in Network OpsDeploy and operate AI/ML models used for fault prediction, alarm correlation, anomaly detection, root-cause analysis, traffic forecasting, and capacity optimizationIntegrate AI outputs into NOC & NOA Digital Ecosystem tools, OSS/NMS, ITSM, and network automation platformsSupport closed-loop and human-in-the-loop automation for incident, change, and performance managementEnsure telecom-grade availability, latency, scalability, and resiliency2. AI Quality Assurance & Network SafetyValidate network data sources (KPIs, counters, alarms, logs, traces, telemetry) for accuracy and completenessTest AI behavior under normal, peak, and failure scenarios across RAN, transport, core, and cloud domainsPrevent AI-driven actions that could cause network instability, service degradation, or SLA breachesDefine and enforce AI quality gates before production rollout3. Monitoring, Drift & Lifecycle ManagementContinuously monitor model performance, confidence, data drift, and concept driftEstablish thresholds to trigger alerts, rollback, retraining, or manual interventionManage model versioning, baselines, and performance history across releases4. Governance, Explainability & ComplianceEnsure AI recommendations are interpretable and actionable for NOC engineersMaintain audit trails for AI decisions affecting network configuration or trafficEnforce security, privacy, and regulatory compliance5. Collaboration & Continuous ImprovementWork with network engineers, data scientists, DevOps, NOC & Operations teamsEmbed AI QA into CI/CD and MLOps pipelinesDrive continuous optimization of AI models based on live network feedbackNATURE OF PROBLEMS ENCOUNTEREDAn AI Operations & QA Professional faces problems driven by dynamic data, probabilistic models, and high operational risk. Common challenges include poor or inconsistent data quality, model performance degradation caused by data and network changes, and managing false positives or missed incidents that erode operator trust. They must ensure AI decisions are explainable and auditable, especially in regulated, mission-critical environments. Additional issues include safely controlling automation to avoid cascading failures, integrating AI with complex legacy systems, and validating models against rare but severe edge cases. Overall, the role centers on managing uncertainty, reliability, and trust in live AI systems.REQUIREMENTSTechnical SkillsStrong understanding of AI/ML fundamentals (model types, training, inference, evaluation metrics)Hands-on experience with MLOps/AIOps tools and practices (model deployment, monitoring, retraining)Data engineering skills: data validation, feature pipelines, data drift detectionExperience with CI/CD, automation, and Infrastructure as CodeProficiency in Python and scripting for testing, monitoring, and automationKnowledge of cloud, edge, and container platforms (Docker, Kubernetes)Familiarity with monitoring, logging, and observability toolsSoft SkillsStrong analytical and troubleshooting mindsetClear communication with both technical and operations teamsAbility to balance innovation with operational risk and reliabilityLevel of Knowledge: 2, 3Level 2 – Experienced: 3-4 years of work experience in the desired/closely related area(s) Level 3 – Solid: 5-7 years of work experience in the desired/closely related area(s)CompetenciesStrong foundation in software build, testing, deployment, and monitoringSolid understanding of version control, automation, and deployment strategiesAbility to manage multiple tasks and prioritize effectivelyWillingness and capability to learn quickly and adapt to new tools and technologiesStrong collaboration skills with a customer-first mindsetClear communication skills to ensure alignment on security, scalability, and reliability requirementsAbility to understand system architecture and interact with multiple application components written in different programming languagesEqual Opportunity EmployerGlobe’s hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.Globe’s Diversity, Equity and Inclusion Policy Commitment can be accessed hereMake Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.