Head of Quality & AI Excellence

LILT (Production)

Boston (MA)

Hybrid

USD 180,000 - 240,000

Full time

8 days ago

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Job summary

LILT is seeking a Director of Quality to own the quality system end to end for AI‑native workflows, including standards, audits, compliance, procedures and continuous improvement.

You will be a hands‑on quality SME and partner with Customer Success, Production, Research and Product to translate customer requirements into measurable criteria and governance, balancing automated, agent‑verified and human‑verified approaches.

Qualifications

  • Hands‑on experience designing, building and running LLM‑based agents in production.
  • Experience turning quality data into actionable metrics, preventive controls and improvements.
  • Proven track record improving LLM output quality.
  • Leading audits, compliance programs and maintaining processes and procedures.
  • Experience managing and developing people.
  • Customer advisory and cross‑functional partnership with stakeholders.

Responsibilities

  • Evaluate, design and build reliable Quality Agents with well‑defined tools and clear, structured instructions.
  • Define evaluation criteria, test sets and success metrics for agent performance in production.
  • Drive adoption across teams and show measurable reductions in manual quality effort.
  • Work with Research and Product on ADQ, LLM model improvements, AI Review and AI QA.
  • Own audits and ISO certification processes and manage evidence collection.

Skills

Agent design & build
Data‑driven quality mgmt
LLM model improvement
Audits & compliance
People leadership

Job description

LILT is seeking a Director of Quality to own the quality system end to end for AI‑native workflows, including standards, audits, compliance, procedures and continuous improvement.

You will be a hands‑on quality SME and partner with Customer Success, Production, Research and Product to translate customer requirements into measurable criteria and governance, balancing automated, agent‑verified and human‑verified approaches.

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