Process Engineer

The Process Engineer is a senior operational role within the Process Engineering function, responsible for the governance, monitoring and continuous improvement of AI-driven policy lifecycle processes. This role does not build or configure AI models, that sits with a dedicated engineering function, but instead acts as the operational owner of AI process performance, ensuring processes run accurately, compliantly and in alignment with business expectations. The Process Engineer translates operational observations into structured requirements, drives HITL reduction through evidence-based improvement cycles, and ensures all AI-driven processes are auditable, traceable and fit for purpose.

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Key Responsibilities:

• Process Monitoring & Performance Oversight

Monitor AI process performance on a daily and weekly basis across assigned policy lifecycle domains, tracking accuracy rates, HITL volumes, process uptime and volumes processed against expected. Maintain visibility dashboards and escal accuracy drops, HITL increases or process failures promptly. Track performance against SLA expectations and flag deviations.

• Agent Performance Oversight

Monitor the behavior and outputs of AI agents operating within assigned processes, identifying patterns of degradation, unexpected behavior or decision quality issues. Maintain a clear picture of agent performance over time and use this to inform improvement requirements.

• HITL Reduction & Continuous Improvement

Analyse HITL intervention patterns to identify where AI processes are underperforming. Raise structured, detailed business and functional requirements to the AI/ML engineering function for model and process improvements. Conduct UAT on new releases before go-live and track HITL trend improvement across enhancement cycles.

• Process Transformation through AI

Define requirements for new AI-enabled processes or the AI augmentation of existing ones. Build and maintain decision trees for AI routing and exception handling. Develop AI-ready SOPs that are structured, unambiguous and formatted for both AI consumption and human oversight. Support the end-to-end transition of manual processes into AI-driven workflows.

• Process Audit & Traceability

Perform regular sample-based QA reviews of AI process outputs. Conduct root cause analysis when quality issues or failures occur. Define and raise auditability and traceability requirements — including decision logs, confidence scores and HITL audit trails — to the engineering function. xqbhyrx Ensure processes meet internal compliance and operational audit standards.

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