Own predictable, transparent delivery for Enrich AI across infrastructure, AI quality, and integration initiatives, ensuring clear priorities and minimal delivery friction.
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.
Current delivery surface area includes:
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)
DataHub 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 2026-01-05)
Current code base surface area (from GitHub READMEs):
Backend/API: enrich-ai is an Nx monorepo POC aiming to replace existing Enrich APIs,
building a federated GraphQL graph (Node.js/NestJS/Prisma; GraphQL federation/Apollo
Router; GraphQL 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.
Key responsibilities
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 applicab
le),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 DataHub
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,roll backs, 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, reviewab
le 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 b
i-weekly CTO-ready highlights (accomplished/ planned / risks / decisions).
Preferred experience (strong plus)
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 GraphQL/Graph federation APIs and modern TypeScript backends (Nx monorepos, NestJS).
Familiarity coordinating frontend delivery in React ecosystems (Material UI / component libraries) and feature
-flag rollouts.
Key relationships
Engineering lead (Enrich)
SRE / platform engineering
DIM / DataHub team (dependency partner)
Product and Operations stakeholders
Professional Services (incl. India team)
.
