AI Full Stack Engineer We’ve built an AI-native internal platform that powers every aspect of our Amazon brand management business. AI isn’t a feature — it’s the backbone. - LLMs classify and respond to inbound communications - AI generates pre-call intelligence briefs from raw enrichment data - A RAG system feeds context into every generation pipeline - An AI checkpoint system audits all generated content against quality gates The platform is already live and scaling fast: - 17+ background services - 130+ frontend pages - 214 backend services - 184 database tables - Dozens of autonomous AI pipelines We’re hiring an engineer who operates at the intersection of AI and production systems. You’ll build, optimize, and scale AI-powered infrastructure across the full stack. What You’ll Build & Scale AI Communication Pipelines - Classify inbound messages by category, intent, urgency, and tone - Generate contextual responses using enrichment data - Implement human approval gates AI-Powered Sales Intelligence - Transform raw enrichment data into structured pre-call briefs - Generate: background, pain hypotheses, talking points, rapport hooks RAG System - Vector database with embeddings - Markdown-aware chunking - Async ingestion workers - Semantic search API Trend Intelligence Engine - Process RSS feeds, social media, video platforms, and search trends - Generate reports, forecasts, and content drafts - Run autonomously on scheduled jobs Content Quality Pipeline - Multi-agent system (outline → audit → generate) - Binary quality gates (PASS/FAIL with citations) - Supports multiple content formats Automated Lead Qualification - Enrich leads with product data and market insights - AI scoring and qualification grading - Automated audit reports AI Executive Assistant - Slack operations - Scheduling workflows - Email triage and follow-ups

AI Full Stack Engineer We’ve built an AI-native internal platform that powers every aspect of our Amazon brand management business. AI isn’t a feature — it’s the backbone. - LLMs classify and respond to inbound communications - AI generates pre-call intelligence briefs from raw enrichment data - A RAG system feeds context into every generation pipeline - An AI checkpoint system audits all generated content against quality gates The platform is already live and scaling fast: - 17+ background services - 130+ frontend pages - 214 backend services - 184 database tables - Dozens of autonomous AI pipelines We’re hiring an engineer who operates at the intersection of AI and production systems. You’ll build, optimize, and scale AI-powered infrastructure across the full stack. What You’ll Build & Scale AI Communication Pipelines - Classify inbound messages by category, intent, urgency, and tone - Generate contextual responses using enrichment data - Implement human approval gates AI-Powered Sales Intelligence - Transform raw enrichment data into structured pre-call briefs - Generate: background, pain hypotheses, talking points, rapport hooks RAG System - Vector database with embeddings - Markdown-aware chunking - Async ingestion workers - Semantic search API Trend Intelligence Engine - Process RSS feeds, social media, video platforms, and search trends - Generate reports, forecasts, and content drafts - Run autonomously on scheduled jobs Content Quality Pipeline - Multi-agent system (outline → audit → generate) - Binary quality gates (PASS/FAIL with citations) - Supports multiple content formats Automated Lead Qualification - Enrich leads with product data and market insights - AI scoring and qualification grading - Automated audit reports AI Executive Assistant - Slack operations - Scheduling workflows - Email triage and follow-ups

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up with a focus on Marketing. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams. • Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value. • Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces. • Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions. • Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms. • Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption. • Participate in production support and incident response for owned systems, helping define and uphold reliability and operational best practices. • Mentor AI team members through code reviews, design feedback, and hands-on guidance, raising the overall quality bar of the team.

• The AI Engineer is responsible for building, deploying, and operating production-grade AI and Agentic AI solutions across the enterprise. • This is a hands-on engineering role focused on implementing LLM-powered applications, orchestration and multi-agent workflows, and secure API-driven integrations using Azure AI Foundry and Azure-native services. • The role works across the full AI lifecycle —from system design and development to production operations—ensuring solutions are secure, scalable, observable, and governed. • The AI Engineer partners closely with Engineering Leads, architects, data science teams, and platform/security stakeholders to translate AI use cases into reliable, enterprise ready systems.