AI/ML Engineer

Role overview

Mandatory and additional skills

• Mandatory: Google Cloud AI Stack; core AI and machine learning; programming and frameworks; telecom domain data and architecture; applied telecom AI use cases.
• Good to have: MLOps and containerization; cloud architecture; GCP data engineering.

Technical responsibilities

• Build, code, and unit-test solution components.
• Design, develop, evaluate, and operationalize AI and agentic capabilities.
• Model, ingest, transform, secure, and serve data.
• Design, provision, configure, migrate, and tune platform components.
• Implement telecom-specific processes, rules, standards, and validations.
• Follow approved designs and engineering standards; maintain current proficiency, certifications, or hands-on practice with primary tools.

Functional responsibilities

• Analyze and validate AI/ML use cases, agent workflows, evaluation criteria, and responsible-AI expectations.
• Translate stakeholder needs into functional requirements, user stories, acceptance criteria, and traceable deliverables.
• Facilitate requirement reviews, walkthroughs, demonstrations, UAT coordination, and business sign-off.
• Maintain functional specifications, process flows, decisions, risks, dependencies, and status reporting.
• Clarify assigned requirements with stakeholders and escalate risks early.
• Support release readiness, user enablement, knowledge transfer, and post-go-live stabilization.

Experience and qualifications

• The description states 10+ years of experience in its opening details.
• The functional description separately requests approximately 4–7 years of relevant industry experience aligned to the Regular Engineer band.
• Requires analytical, debugging, and problem-solving ability; strong verbal and written communication; and working knowledge of relevant AI standards, compliance, and business processes.