AI Operations Engineer

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

DUTIES & RESPONSIBILITIES

1. AI Operationalization in Network Ops

• Deploy and operate AI/ML models used forfault prediction, alarm correlation, anomaly detection, root-cause analysis, traffic forecasting, and capacity optimization

• Integrate AI outputs intoNOC & NOA Digital Ecosystem tools, OSS/NMS, ITSM, and network automation platforms

• Supportclosed-loop and human-in-the-loop automation for incident, change, and performance management

• Ensure telecom-gradeavailability, latency, scalability, and resiliency

2. AI Quality Assurance & Network Safety

• Validatenetwork data sources (KPIs, counters, alarms, logs, traces, telemetry) for accuracy and completeness

• Test AI behavior undernormal, peak, and failure scenarios across RAN, transport, core, and cloud domains

• Prevent AI-driven actions that could causenetwork instability, service degradation, or SLA breaches

• Define and enforce AI quality gates before production rollout

3. Monitoring, Drift & Lifecycle Management

• Continuously monitormodel performance, confidence, data drift, and concept drift

• Establish thresholds to triggeralerts, rollback, retraining, or manual intervention

• Manage model versioning, baselines, and performance history across releases

4. Governance, Explainability & Compliance

• Ensure AI recommendations areinterpretable and actionable for NOC engineers

• Maintain audit trails for AI decisions affecting network configuration or traffic

• Enforce security, privacy, and regulatory compliance

5. Collaboration & Continuous Improvement

• Work withnetwork engineers, data scientists, DevOps, NOC & Operations teams

• Embed AI QA intoCI/CD and MLOps pipelines

• Drive continuous optimization of AI models based on live network feedback

NATURE OF PROBLEMS ENCOUNTERED

AnAI Operations & QA Professional faces problems driven bydynamic data, probabilistic models, and high operational risk. Common challenges include poor or inconsistent data quality, model performance degradation caused bydata and network changes, and managing false positives or missed incidents that erode operator trust. They must ensure AI decisions areexplainable 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 managinguncertainty, reliability, and trust in live AI systems.

REQUIREMENTS

Technical Skills

• Strong understanding ofAI/ML fundamentals (model types, training, inference, evaluation metrics)

• Hands-on experience withMLOps/AIOps tools and practices (model deployment, monitoring, retraining)

• Data engineering skills:data validation, feature pipelines, data drift detection

• Experience withCI/CD, automation, andInfrastructure as Code

• Proficiency inPython and scripting for testing, monitoring, and automation

• Knowledge ofcloud, edge, and container platforms (Docker, Kubernetes)

• Familiarity withmonitoring, logging, and observability tools

Soft Skills

• Strong analytical and troubleshooting mindset

• Clear communication with both technical and operations teams

• Ability to balance innovation withoperational risk and reliability

Level of Knowledge: 2, 3
Level 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)

Competencies

• Strong foundation in software build, testing, deployment, and monitoring

• Solid understanding of version control, automation, and deployment strategies

• Ability to manage multiple tasks and prioritize effectively

• Willingness and capability to learn quickly and adapt to new tools and technologies

• Strong collaboration skills with a customer-first mindset

• Clear communication skills to ensure alignment on security, scalability, and reliability requirements

• Ability to understand system architecture and interact with multiple application components written in different programming languages

Equal Opportunity Employer
Globe’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 accessedhere

Make 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.