Operations Department Lead (AI-First)

As a Domain Operations Lead, you will move away from traditional manual processing to lead an AI-first, human-in-the-loop operating model. Your mandate is to ensure that AI executes by default while you govern the logic, judge exceptions, and protect client trust through decision integrity.

1. Key Responsibilities

Operations Future Roles

A. Delivery Integrity (TAT & Efficiency)

• WIP Management:

  Conduct real-time monitoring of sub-queues to identify and unblock "stuck" cases within the automated workflow.

• Priority Allocation:

  Ensure WIP checks are intelligently allocated across queues based on priority levels and client-specific SLAs.

• Target Monitoring:

  Track automated "resources" (AI bots, Voice AI) to ensure they achieve daily throughput targets.

• Capacity Planning:

  Manage human-in-the-loop resource availability for the manual exception queue and high-complexity red-case handling.

B. Quality & Decision Trust (Accuracy)

• Root Cause Analysis (RCA):

  Perform deep-dive analysis of color changes and rework triggers (both AI-triggered and manual) to eliminate systemic errors.

• Feedback Loops:

  Provide structured feedback to Ops and Tech teams to refine AI calling results and improve closure quality.

• Decision Auditing:

  Conduct sample audits on AI-closed "Green" cases to ensure no hallucinations or incorrect classifications occurred.

• Escalation Resolution:

  Directly manage and resolve customer escalations, using findings to drive down overall escalation volume.

C. Continuous Automation & Evolution

• Process Re-engineering:

  Proactively identify manual legacy steps within the domain that can be replaced by AI or other automation tools.

• SKU Automation:

  Drive end-to-end automation for specific SKUs, ensuring they transition to a "straight-through processing" model.

• Logic Verification:

  Collaborate with the PVT team to ensure domain-specific rules are accurately configured in the Rule Engine.

D. Data Ecosystem & Source Reliability

• Vault Management:

  Own and maintain the Education and Employment Research Databases to ensure data reliability.

• Internal Closure Expansion:

  Strategy development to increase internal closures and reduce dependency on external manual verification.

• Source Liaison:

  Manage relationships with verification sources (Universities, Employers, Govt bodies) to secure faster response times.

2. Required Skills & Competencies

Technical & Analytical Skills

• AI & Automation Literacy:

  Understanding of Rule Engines, OCR accuracy metrics, and how to troubleshoot AI calling/Voice AI outputs.

• Data Analytics:

  Proficiency in dashboard monitoring and the ability to interpret MIS to find automation gaps.

• Root Cause Investigation:

  Highly developed analytical skills to distinguish between data errors, model hallucinations, and process failures.

• Database Management:

  Experience in governing large datasets (Research Databases/Vaults) for verification purposes.

Behavioral & Soft Skills

• Judgment-Led Decision Making:

  Ability to handle complex "red cases" where AI logic is insufficient and human discretion is required.

• Tech-Native Mindset:

  A "technology-first" approach to problem-solving, preferring system fixes over manual patches.

• Stakeholder Influence:

  Ability to translate operational failures into technical feedback for Engineering and Product teams.

• Adaptability:

  Comfortable in a shifting landscape where roles are redesigned from manpower-driven to tech-native.

3. Experience & Qualifications

• Domain Expertise:

  5 years of experience in high-volume data operations environment.

• Education:

  Bachelors/Masters degree with a focus on Business Operations, Data Science, or a related analytical field.

• Previous Role:

  Experience in Quality Governance, Process Excellence, or Service Delivery is preferred.