Sr Specialist Quality/M&P/Process - AI Training Manager

This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.

At AT&T, we empower leaders to drive change in a fast-evolving, connected world. Your strategic vision will help serve customers and transform lives through innovative solutions and impactful connections.

The Sr Specialist Quality/M&P/Process - AI Training Manager is responsible for overseeing the training, validation, and continuous improvement of Agentic Capabilities—AI-powered agents designed to autonomously process a variety of ticket types within workflow management systems. This role owns AI agent quality: ensuring reliable, high-quality outcomes; rapidly reviewing and resolving exception (“fallout”) tickets; applying corrections and re-ingesting updates; improving training data and fine-tuning artifacts; updating the agent knowledge base; and rerunning tickets to validate fixes—continuously strengthening agentic capabilities.

Key Responsibilities

Agentic Capability Management

• Monitor the performance of Agentic Capabilities as they autonomously process various ticket types.
• Ensure seamless integration of AI agents into new or existing workflows, optimizing for efficiency and accuracy.

Fallout Review & Correction

• Review fallout tickets (cases where AI agents cannot resolve issues) within a workflow management tool.
• Diagnose root causes, make necessary corrections, and re-ingest updated information to the AI system.
• Ensure all fallout tickets are actioned within a 48-hour window; unresolved tickets revert to the human-worked queue.

Training & Continuous Improvement

• Analyze fallout patterns to identify knowledge gaps, process inefficiencies, or opportunities for AI improvement.
• Develop and implement training protocols to enhance Agentic Capabilities, leveraging prompt engineering, model validation, and knowledge base updates.
• Collaborate with cross-functional teams (product, engineering, support) to align AI behaviors with business needs and compliance requirements.

Knowledge Base & Documentation

• Maintain and update the agent knowledge base, ensuring accurate, current, and comprehensive content for AI agents.
• Document training methodologies, ticket resolutions, and process improvements for knowledge sharing and auditing.

Quality Assurance & Compliance

• Validate AI performance through systematic review, testing, and user/stakeholder feedback.
• Ensure all processes comply with regulatory standards, ethical guidelines, and company policies.

Reporting & Communication

• Track and report on key metrics: ticket resolution rates, fallout frequency, review turnaround times, and AI improvement outcomes.
• Continuous Improvement practices
• Communicate insights, best practices, and recommendations to stakeholders and leadership.

Skills & Qualifications

• Understanding of the business function and M&Ps; align AI behavior with policy and process; plus knowledge of supported workflows and tools with experience operating the systems involved (e.g., ticketing/workflow platforms).
• Strong analytical and problem-solving skills with meticulous attention to detail; proven root cause analysis (RCA) capability.
• General AI literacy and understanding of agentic systems; basic prompt engineering (iteration, testing, versioning).
• Ability to manage fallout within SLAs, triage tickets, and drive rapid resolution; strong prioritization in fast-paced environments.
• Observability: proficiency with logs, metrics, dashboards, and alerts; define and track quality KPIs (accuracy, fallout rate, MTTR).
• Basic scripting understanding to automate corrections, content re-ingestion, and validation workflows.
• Knowledge base authoring and maintenance; clear documentation of training methods, resolutions, and changes for auditability.
• Compliance/data privacy/ethical guidelines awareness; maintain auditable processes and change logs.
• Effective communication: synthesize findings, report metrics, and present recommendations to stakeholders.

Preferred Skills

• Advanced observability (distributed tracing, SLO/SLA design) and incident response practices.
• Experiment tracking and ML operations tooling, feature flags, canary/rollback strategies.
• Familiarity with fine-tuning pipelines, retrieval/RAG, vector databases, and content ingestion pipelines.
• SQL/BI tools for advanced analytics and dashboarding; ability to build executive-ready reports.
• Version control (Git) for prompts, KB content,