About The Role

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

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

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

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

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

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

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 Finance leaders to embed AI fluency into their team cultures

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. 