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.

ERA — QAD's Enterprise Resource Action platform — is the domain intelligence layer that sits between any ERP and any AI agent. It encodes what manufacturing data means, governs what agents are permitted to do, and makes every autonomous decision traceable and accountable.

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.

Core Responsibilities:

1. Data Foundation & Semantic Layer

• 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 BigQuery datasets (GLDetails, ARR_Waterfall, EarnedRevenue, etc.) and the financial concepts agents reason over

• Partner with the data engineering team and other functions on dbt model governance and Fivetran pipeline integrity

2. Agent Architecture & Process Design

• Determine the build methodology for process agents — frameworks, orchestration patterns, tool use conventions, handoff protocols between agents

• Sequence the agent build roadmap (which of the 22 agents come first, dependencies, parallel workstreams)

• 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

3. Agent Operations & Lifecycle 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

4. Visibility & Monitoring Platform

• 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 you and Sanjay on a regular cadence

5. AI Risk Management & Internal Controls

• 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

6. Finance Organization Training & Change Management

• 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 Kisha, Navielle, Drew, and Chris to embed AI fluency into their team cultures 

7. Process Reengineering

• Lead the Finance process rewrite initiative — working function-by-function with AP, AR, FP&A, Accounting, and CS to document current-state flows and design agent-native future-state flows

• 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

• 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

• Your health and well being are important to us at QAD. We provide programs that help you strike a healthy work-life balance.
• Opportunity to join a growing business, launching into its next phase of expansion and transformation.
• Collaborative culture of smart and hard-working people who support one another to get the job done.
• An atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.
• Compensation packages based on experience and desired skill set

About QAD:

QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.

QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.