Technical Platform Expert - Nyc

Legora Legora · Vertical AI · New York, NY · Customer Enablement

This role is a senior technical expert responsible for deep-dive investigations and root cause analysis of complex issues within Legora's AI-native legal workspace. The expert will act as the final technical authority, bridging the gap between platform specifications and customer experience, focusing on investigative judgment and platform mastery rather than code writing. Responsibilities include handling deep technical escalations, validating the platform before release, detecting systemic events, serving as an AI subject-matter expert, and advocating for the support function's perspective in product and engineering discussions. The role also involves driving technical root cause in incidents, building diagnostic tooling, and mentoring specialists, operating within a global follow-the-sun coverage model.

What you'd actually do

  1. Own the hardest problems: Take the deepest technical escalations from across the function and drive them to root cause through rigorous log, data, and system-level analysis.
  2. Validate the platform before it ships: Support beta testing and pre-GA investigations. Identify where real platform behavior diverges from the spec, pressure-test workflows under real conditions, and deliver findings to Product Operations with clear reproduction steps before they become customer-facing incidents.
  3. Detect and declare systemic events: Identify when individual tickets share a common root cause and trigger coordinated incident response before volume makes the pattern undeniable. Own the signal that turns a cluster of cases into a declared Sev1 or Sev2 incident.
  4. Be the AI subject-matter expert: Serve as the expert on platform behavior and AI output patterns, including accuracy, retrieval quality, document processing, and anomalous output.
  5. Be the support function's voice in Product and Engineering: Bring the real-world pattern of platform failures into product reviews, release planning, and engineering discussions. Surface what the support tiers see that Product Operations does not, and advocate for the diagnostic tooling, observability improvements, and platform fixes that make the function faster and the product more reliable.

Skills

Required

  • Deep technical support, solutions, or platform-specialist experience
  • Root cause analysis using logs, data, and systems
  • Understanding of LLM and AI system behavior and failure modes
  • Clear technical communication for product and customer-facing teams
  • Cross-functional collaboration and advocacy
  • Handling major escalations under pressure
  • Pattern recognition across cases
  • Diagnostic tooling and test framework development

Nice to have

  • Background in legal AI
  • Document processing expertise
  • Retrieval-augmented systems experience

What the JD emphasized

  • final technical authority
  • not writing code
  • gap between what the product promises and what customers experience
  • deepest technical escalations
  • rigorous log, data, and system-level analysis
  • real platform behavior diverges from the spec
  • pressure-test workflows under real conditions
  • common root cause
  • declared Sev1 or Sev2 incident
  • platform behavior and AI output patterns
  • accuracy, retrieval quality, document processing, and anomalous output
  • real-world pattern of platform failures
  • diagnostic tooling, observability improvements, and platform fixes
  • technical counterpart to the Principal Platform Advisor on Sev1 and Sev2 events
  • owning real-time diagnosis
  • Engineering liaison
  • technical path to resolution
  • confirmed defects with clean reproduction steps and impact assessment
  • diagnostic tooling, test frameworks, and automation
  • escalation trends back into product and process improvements
  • final-filter reviewer
  • occasional ad-hoc requirements outside the standard schedule
  • strong technical bent
  • rigorous about the root cause
  • logs, data, and systems are where you are at home
  • understand how LLM and AI systems behave, including how failure modes present to users
  • translate technical findings clearly
  • represent the support function's perspective in engineering and product conversations
  • translate field patterns into product arguments that land
  • composed under pressure
  • experience working major escalations in parallel
  • think in patterns across cases
  • act on a signal before it becomes a problem the customer names first

Other signals

  • AI-native workspace
  • analysing thousands of documents in minutes
  • powering end-to-end workflows
  • legal AI
  • LLM and AI systems behaviour
  • accuracy, retrieval quality, document processing, and anomalous output