Delivery Manager

Overview

Enrich AI is Rezolve’s product data enrichment platform. It uses AI/LLM driven orchestration to enrich product catalog data (attributes, classifications, metadata) to improve search quality and product discovery.

• Infrastructure stabilization/scaling and environment provisioning (working with SRE + vendor such as Aiven).
• AI output quality improvements (audits, golden dataset testing).
• Orchestration optimization (reduce LLM calls / cost-to enrich).
• Data Hub integration for automated data flows (cross-project dependency on DIM).
• Rules system (post-enrichment processing) and related frontend ops workflows.
• Distributed team delivery (incl. Rezolve India team onboarding started ).

• Backend/API:
  enrich-ai is an Nx monorepo POC aiming to replace existing Enrich APIs, building a federated Graph
  
  QL graph (Node.js/NestJS/Prisma; Graph
  
  QL federation/Apollo Router; Graph
  
  QL Yoga; Pothos; tRPC).
• UI integration:
  command-center-enrich is a React 18 app/plugin-style repo (Material UI + styled-components) that integrates an Enrich console package into Command Center and is enabled via feature flag.
• Own predictable, transparent delivery for Enrich AI across infrastructure, AI quality, and integration initiatives; ensure the team can plan, execute, and ship with clear priorities, minimal delivery friction, and reliable stakeholder communication.

• Delivery planning & execution
  Run weekly planning/triage and drive a consistent delivery cadence aligned to the team’s “Weekly Highlight Reports” structure. Maintain delivery plans in Jira (including Advanced Roadmaps plan where applicable), ensuring scope, sequencing, and dependencies are explicit. Convert goals/initiatives into executable epics/stories with clear acceptance criteria and measurable outcomes.
• Dependency & blocker management (critical path)
  Actively manage cross-team dependencies, notably Data Hub Enrich integration blocked by DIM work. Set up explicit dependency tracking (Jira links, dependency board, weekly check-ins) and publish status/ETAs with confidence levels.
• Release & operational readiness
  Coordinate releases and operational readiness with SRE/Operations; ensure run books, rollbacks, and monitoring/alerting expectations are met. Drive risk reviews for infra changes, vendor constraints, and environment provisioning.
• Quality and AI outcomes management
  Ensure AI quality work is planned and communicated with clear, reviewable outcomes. Ensure the team can report AI improvements in a way leadership can understand (quality, cost, throughput).
• Jira/Confluence hygiene + reporting
  Ensure Jira reflects reality (status discipline, WIP limits, aging work review, clear definitions of done). Produce crisp weekly delivery updates and bi-weekly CTO-ready highlights (accomplished/ planned / risks / decisions).

• AI/ML product delivery experience (MLOps, model/LLM quality evaluation, experiment cadence).
• Data pipeline/integration delivery experience (ETL, data platforms, system-to-system integrations).
• Experience with PIMs (Product Information Management) / PIM workflows.
• Familiarity with CI/CD and cloud vendor management.
• Familiarity delivering Graph
  
  QL/Graph federation APIs and modern Type Script backends (Nx monorepos, NestJS).
• Familiarity coordinating frontend delivery in React ecosystems (Material UI / component libraries) and feature flag rollouts.

• Engineering lead (Enrich)
• SRE / platform engineering
• DIM / Data Hub team (dependency partner)
• Product and Operations stakeholders
• Professional Services (incl. India team)

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