Lead AI Specialist (Finance Operations)

QAD, Inc.; Mumbai; On-site; INR 3,500,000 - 6,500,000; Full time

Benefits offered by this job

Health & well-being programs

Growth opportunities

Collaborative culture

Job summary

QAD, Inc. is seeking an experienced leader to drive the technical design, deployment, and management of Finance AI agents. You will bridge finance operations, data engineering, and AI systems to build secure, auditable agent workflows that optimize finance processes.

You will own architecture decisions, from LLM selection to memory design and context management, while ensuring governance, auditability, and ROI. This role requires strong collaboration across finance, data, and security teams.

Qualifications

• Hands-on experience building and managing production-grade LLM-based agents in a finance context.
• Strong understand­ing of finance controls and audit requirements.
• Experience bridging finance operations, data engineering, and AI/ML systems.

Responsibilities

• Lead the design, deployment, and operational management of Finance AI agents.
• Define the semantic layer grounding AI in financial realities.
• Architect agent systems: memory, retrieval, and context management.
• Operate agent registry: versioning, deployment, and change control.
• Monitor agent performance, drift, and prompt updates.
• Implement an AI control center for real-time visibility and ROI reporting.
• Collaborate with infosec on data access controls.

Skills

LLM-based agents

Finance operations

Data engineering

AI/ML systems

Change management

Communication

Tools

BigQuery

dbt

Fivetran

LLM tooling

Job description

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.

Job Description

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 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

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.

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 management ona 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

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 Finance leaders to embed AI fluency into their team cultures

7. Process Reengineering

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

Qualifications

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.

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

Additional Information

• 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 withChampion 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.