Lead AI Specialist (Finance Operations)

QAD is a leading provider of ERP solutions purpose-built for manufacturing industries — automotive, life sciences, food & beverage, high tech, and industrial. Serving thousands of global manufacturers, QAD's Adaptive Manufacturing Cloud helps companies operate with greater precision, agility, and intelligence.

Enterprise software is entering a third era. The first gave manufacturers a System of Record — ERP that answered 'what do we have and what did we commit to?' The second gave them Data Infrastructure — the ability to move, analyse, and query that data at scale. The third era is domain-specific intelligence: AI agents that can act autonomously on manufacturing data, but only if that data has been given the context, relationships, and governed rules that allow an agent to reason correctly.

This position will lead the technical design, deployment, and operational management of QAD's Finance AI agents. The role bridges finance operations, data engineering, and AI systems to build secure, high-performing, and auditable agent-assisted workflows that optimize finance processes.

Responsibilities

Define and own the context tables (business glossary, entity definitions, metric hierarchies) that ground AI agents in QAD's financial reality.; Build and maintain the semantic layer — the translation between raw Google Cloud BigQuery datasets and the financial concepts agents reason over; Partner with the data engineering team and other functions on dbt model governance and Fivetran pipeline integrity; Determine the build methodology for process agents — frameworks, orchestration patterns, tool use conventions, handoff protocols between agents; Sequence the agent build roadmap.; Design the human-in-the-loop thresholds — when an agent escalates vs. executes autonomously; Own the technical architecture decisions: LLM selection, retrieval strategy, memory design, context window management; Run the agent registry — versioning, deployment, deprecation, and change control for all live agents; Manage agent performance over time: drift detection, accuracy degradation, prompt updates as business rules change; Own the incident response protocol when an agent produces an incorrect output or takes an unintended action; Coordinate agent updates when underlying data schemas, business logic, or finance policies change; Build or procure a Finance AI Control Center — a dashboard giving real-time visibility into which agents are running, what they've done, error rates, and escalation queues; Define and track agent KPIs: task completion rate, exception rate, cycle time vs. manual baseline, cost per transaction; Create an audit log for every agent action — who initiated it, what data was touched, what output was produced; Report agent health and ROI to management on a regular cadence; Design and own the internal control framework for AI in Finance; Define approval hierarchies: what an agent can do autonomously vs. what requires human sign-off (e.g., any journal entry over $X requires controller review); Partner with infosec on data access controls; Design and deliver the Finance AI literacy curriculum — from foundational concepts to role-specific agent interaction training; Build the change management playbook for the transition: how teams move from manual processes to agent-assisted workflows; Create and maintain agent user guides — what each agent does, its limitations, how to override it, and how to report issues; Work with Finance leaders to embed AI fluency into their team cultures; Lead the Finance process rewrite initiative; Identify which steps in each process are agent-executable vs. requiring judgment, and draw those boundaries explicitly; Ensure redesigned processes still satisfy internal control requirements and auditability standards; Feed process redesign outputs back into agent specifications and the semantic layer

Requirements

Minimum 8 years of experience. Hands-on experience building and managing LLM-based agents in production environments. Background spanning finance operations, data engineering, and AI/ML systems.; Computer skills: BigQuery, dbt, Fivetran, SQL, Python, LLM orchestration frameworks, data pipeline management.; Deep understanding of financial controls, finance operational process flows, and audit requirements. Expertise in LLM & Agentic Architecture, and Change Management & Training; Background spanning finance operations + data engineering + AI/ML systems; Has built or managed LLM-based agents in a production environment, not just POCs; Understands financial controls, finance operational process flows and audit requirements — not just a technologist; Strong communicator who can bridge your finance leads and the technical implementation tea