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QAD

SaaS cloud ERP for manufacturers

Lead AI Specialist - Finance Operations

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-timeMumbai, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • A minimum of 8 years of experience is required.
  • Hands-on experience building and managing LLM-based agents in production environments is required.
  • Experience spanning finance operations, data engineering, and AI/ML systems is required.
  • Computer skills in BigQuery, dbt, Fivetran, SQL, Python, LLM orchestration frameworks, and data pipeline management are required.
  • A deep understanding of financial controls, finance operational process flows, and audit requirements is required.
  • Expertise in LLM and agentic architecture, change management, and training is required.
  • Experience bridging finance leads and technical implementation teams is required.

Responsibilities

  • Define and own the context tables, including business glossaries, entity definitions, and metric hierarchies, that ground AI agents in the company's financial context.
  • Build and maintain the semantic layer translating raw Google Cloud BigQuery datasets into financial concepts that agents can 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, including frameworks, orchestration patterns, tool-use conventions, and handoff protocols between agents.
  • Sequence the agent-build roadmap.
  • Design human-in-the-loop thresholds that determine when an agent escalates versus acts autonomously.
  • Own technical architecture decisions, including LLM selection, retrieval strategy, memory design, and context-window management.
  • Run the agent registry, including versioning, deployment, deprecation, and change control for live agents.
  • Manage agent performance over time through drift detection, accuracy-degradation monitoring, and prompt updates as business rules change.
  • Own the incident-response protocol for incorrect agent outputs or unintended actions.
  • Coordinate agent updates when underlying data schemas, business logic, or finance policies change.
  • Build or procure a Finance AI Control Center dashboard providing real-time visibility into running agents, their actions, error rates, and escalation queues.
  • Define and track agent key performance indicators, including task completion rate, exception rate, cycle time versus manual baseline, and cost per transaction.
  • Create an audit log for every agent action, recording who initiated it, what data was touched, and what output was produced.
  • Report agent health and return on investment to management on a regular cadence.
  • Design and own the internal control framework for AI in Finance.
  • Define approval hierarchies that determine what agents may do autonomously and what requires human sign-off, including controller review for journal entries above a defined threshold.
  • Partner with information security 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 transitioning teams from manual processes to agent-assisted workflows.
  • Create and maintain agent user guides covering each agent's capabilities, limitations, override process, and issue-reporting process.
  • 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 versus requiring judgment, and define those boundaries explicitly.
  • Ensure redesigned processes continue to satisfy internal-control requirements and auditability standards.
  • Feed process-redesign outputs back into agent specifications and the semantic layer.

About the Company

QAD

SaaS cloud ERP for manufacturers

QAD provides cloud-based ERP and manufacturing software that helps manufacturers and supply chain operators run operations more efficiently and resiliently. Its SaaS platform includes planning, production, procurement, inventory, and logistics modules hosted in the cloud, delivering real-time data and global collaboration. The software combines finance, manufacturing execution, demand planning, and supplier management in industry-tailored, cloud-first solutions to forecast demand, optimize production, and respond to disruptions. Its focus on manufacturing and supply chain optimization across 100 countries, with an adaptive cloud platform, sets it apart, and its goal is to help customers become Adaptive Manufacturing Enterprises that adjust quickly to changing conditions while delivering reliable service.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Santa Barbara, CA
Founded
1979

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