Expert, AI Engineering

Lead the roadmap, delivery, and governance of Finance applications, integrations, and intelligent automation. You will architect and implement applied AI and rule‑based automations, design secure integrations and agentic orchestration across platforms, and elevate Finance process efficiency and control.

1. Finance Applications Ownership

• Own the end-to-end lifecycle for Finance systems and tooling (e.g., ERP/EPM, Finance add-ons, integration services, AI/automation services used by Finance).
• Translate Finance AI strategy into a systems roadmap with clear milestones, controls, and business benefits.
• Lead solution design, configuration, integration, testing, release/change management, performance, reliability, and cost optimization.
• Define and maintain standards, documentation, and knowledge base for Finance systems.

2. AI & Intelligent Automation

• Identify, design, and operationalize AI-enabled and rule-based automation use cases across Finance
• Establish prompting/interaction patterns, evaluation and guardrails for AI components (quality, security, bias/hallucination checks), and observability (quality metrics, cost tracking, drift, auditability).
• Implement retrieval and search over Finance knowledge assets with appropriate access controls and privacy.
• Ensure solutions are extensible, so future AI capabilities can be incorporated without rework.
• Own the end-to-end lifecycle for Finance systems and tooling (e.g., ERP/EPM, Finance add-ons, integration services, AI/automation services used by Finance).
• Translate Finance AI strategy into a systems roadmap with clear milestones, controls, and business benefits.
• Lead solution design, configuration, integration, testing, release/change management, performance, reliability, and cost optimization.
• Define and maintain standards, documentation, and knowledge base for Finance systems.

2. AI & Intelligent Automation

• Identify, design, and operationalize AI-enabled and rule-based automation use cases across Finance
• Establish prompting/interaction patterns, evaluation and guardrails for AI components (quality, security, bias/hallucination checks), and observability (quality metrics, cost tracking, drift, auditability).
• Implement retrieval and search over Finance knowledge assets with appropriate access controls and privacy.
• Ensure solutions are extensible, so future AI capabilities can be incorporated without rework.

3. Orchestration, Agents & Integrations

• Design and operate agentic automations: implement planners, tool‑use executors, critics/validators, and routers that can call other agents, decompose tasks, and coordinate end‑to‑end Finance workflows.
• Build secure agent‑to‑agent and agent‑to‑tool protocols: context passing, memory/state, retries, timeouts, idempotency, and deterministic fallbacks—with human‑in‑the‑loop for low‑confidence or policy‑gated steps.
• Use context protocols (e.g., MCP or equivalent) to expose ERP/EPM, collaboration, and workflow capabilities as safe tools for agents; enforce least‑privilege access and policy‑based routing.

4. Automation Platforms & Governance

• Scale automation using appropriate platforms to reduce manual effort in finance workflow processes
• Track benefits with KPIs: hours saved, cycle-time reduction, first‑time‑right rate, SLA adherence.

5. Data, Security & Compliance

• Enforce least-privilege, SoD, and Finance compliance across applications, integrations, and AI/automation components.
• Implement data privacy and model/automation governance (PII controls, redaction, retention, content filtering).
• Partner with IT/SecOps and MIS on risk assessments, incident response, audit readiness, and compliance with applicable regulations (e.g., GDPR and Vietnam PDPD/Decree 13/2023).
• Provide mediated access for agents: role‑based tool catalogs, token scoping, sandboxing, content moderation, and SoD enforcement.
• Maintain comprehensive logging & audit of agent decisions, tool calls, prompts/inputs (where compliant), and outputs, aligned to PDPD (Decree 13/2023) and applicable policies.

6. Stakeholder & Vendor Management

• Partner with Finance leadership, Data/IT/Security, Risk/Legal, and external vendors to prioritize backlog and deliver value on schedule.
• Drive training, change management, documentation, and adoption within Finance.

• 5+ years in Banking or Finance Systems, Transformation, or Data/AI Engineering roles supporting Finance.
• Hands‑on ownership of Banking applications (Finance preferred) (configuration/enhancements) and secure API integrations.
• Delivered production rule‑based and applied‑AI automations with guardrails, human‑in‑the‑loop, and observability (quality & cost metrics).
• Built orchestrated/agentic workflows (planner/executor/critic/router patterns), with idempotency, retries/backoff, and timeout/circuit‑breaker strategies.
• Strong on controls & compliance: least‑privilege, SoD, PII/privacy (PDPD/Decree 13, GDPR where applicable), audit trails and evidence.
• Technical stack: SQL + Python (or similar), cloud experience (Azure preferred, GCP/AWS acceptable), CI/CD and version control.
• Proven testing & release discipline: unit/integration/UAT, change management, runbooks, and hyper care.
• Excellent stakeholder communication; English & Vietnamese working proficiency.