Agentic AI in Contact Center

Role Purpose: The Product & Technical Expert (PTE) leads and manages the product and technical tasks of the Agentic AI in Contact Center project across its three components — the virtual AI bot / conversational IVR, the agent-assist, and interaction analytics. The role spans requirements and vendor selection through solution design, integration, AI governance, and delivery oversight, while building lasting in-house capability through knowledge transfer to a nominated internal candidate. Key Responsibilities: · Manage and refine the business and technical requirements across the three components (virtual AI bot / conversational IVR, the agent-assist, and interaction analytics), and maintain the requirements and compliance matrices. · Build and maintain the use-cases and intent-prioritization backlog (Tier 1 automated, Tier 2 AI-assisted, Tier 3 human), translating call-driver data into an automation roadmap. · Define KPIs and success/acceptance criteria. · Support RFP issuance, run the technical evaluation of bidders, lead proofs-of-concept (including Saudi-dialect quality testing), and make sourcing recommendations. · Manage the implementation partner technically — scope, statement of work, deliverables, and quality. · Manage the conversational and agentic design: open intent capture, dialog flows, prompts, tone/persona, escalation triggers, and warm-handoff behavior. · Manage the design of RAG grounding approach, knowledge-base structure, and guardrail configuration. · Define the target architecture and external/internal integration design across core banking (T24), CRM (MS Dynamics), Cortex, e-channels,OTP, and the Cisco IVR/ACD and speech-recording stack. · Specify APIs, real-time context retrieval, latency targets, and the model-hosting approach. · Advise on model selection (LLM/SLM and routing), RAG, hallucination mitigation, confidence thresholds, and prompt/version management. · Establish the AI governance and guardrail framework aligned to SAMA and PDPL — human-in-the-loop for sensitive flows, audit trail, data residency, and PII redaction. · Work with Compliance, Risk, Legal, and Information Security to secure the necessary approvals. · Manage SIT, UAT, and phased go-live; manage risks, issues, and dependencies. · Stand up the analytics / AI-ops monitoring and reporting, and run the post-go-live. · Coach and up-skill the nominated internal candidate (prospective AI-Ops Lead) and the wider team; produce runbooks and operational handover.

Manage and refine the business and technical requirements across the three components (virtual AI bot / conversational IVR, the agent-assist, and interaction analytics), and maintain the requirements and compliance matrices. Build and maintain the use-cases and intent-prioritization backlog (Tier 1 automated, Tier 2 AI-assisted, Tier 3 human), translating call-driver data into an automation roadmap. Define KPIs and success/acceptance criteria. Support RFP issuance, run the technical evaluation of bidders, lead proofs-of-concept (including Saudi-dialect quality testing), and make sourcing recommendations. Manage the implementation partner technically — scope, statement of work, deliverables, and quality. Manage the conversational and agentic design: open intent capture, dialog flows, prompts, tone/persona, escalation triggers, and warm-handoff behavior. Manage the design of RAG grounding approach, knowledge-base structure, and guardrail configuration. Define the target architecture and external/internal integration design across core banking (T24), CRM (MS Dynamics), Cortex, e-channels,OTP, and the Cisco IVR/ACD and speech-recording stack. Specify APIs, real-time context retrieval, latency targets, and the model-hosting approach. Advise on model selection (LLM/SLM and routing), RAG, hallucination mitigation, confidence thresholds, and prompt/version management. Establish the AI governance and guardrail framework aligned to SAMA and PDPL — human-in-the-loop for sensitive flows, audit trail, data residency, and PII redaction. Work with Compliance, Risk, Legal, and Information Security to secure the necessary approvals. Manage SIT, UAT, and phased go-live; manage risks, issues, and dependencies. Stand up the analytics / AI-ops monitoring and reporting, and run the post-go-live. Coach and up-skill the nominated internal candidate (prospective AI-Ops Lead) and the wider team; produce runbooks and operational handover.