• Lead and support the technical design of conversational AI and contact center automation solutions (e.g., Cognigy.AI, Kore.ai, telephony integrations , or similar platforms). • Act as the technical liaison between developers, architects, and business teams — ensuring alignment across design, delivery, and performance goals. • Manage and refine the product backlog , translating complex business needs into actionable user stories. • Evaluate new AI and RAG technologies to improve conversational experiences and agent productivity. • Drive knowledge sharing and best practices across the team. • Organize and lead cross-functional meetings , fostering collaboration and innovation. • Prioritize requests effectively while maintaining clear visibility of deliverables and timelines.

• Build and optimize LLM serving and inference systems for production environments • Improve performance across GPU and CPU pathways • Work on KV cache, memory, storage, and throughput bottlenecks • Design and scale systems that support RAG and retrieval-heavy AI workloads • Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance • Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure

Role Description 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 Qualifications - Production LLM experience (Claude/OpenAI in real systems) - RAG system experience (embeddings, retrieval, chunking, context handling) - 3+ years TypeScript / Node.js - Strong React skills - PostgreSQL (queries, migrations, indexing) - API integrations (REST, OAuth, webhooks) - Linux server experience (SSH, logs, debugging, deployments) Requirements - Strong Pluses - Multi-agent LLM systems - Anthropic Claude expertise - Vector search / embeddings - Slack API experience - Ad platform APIs (Meta, Google, LinkedIn) - LLM observability (cost, tracing, monitoring) - Amazon / eCommerce experience - AI-assisted dev tools (Cursor, Claude Code, etc.) Benefits - Competitive salary based on experience - High-impact role with strong ownership - Opportunity to scale cutting-edge AI systems to world-class level

Role Description We are seeking an AI Developer with strong expertise in Generative AI, Large Language Models (LLMs), and prompt engineering to design, build, and optimize intelligent systems. The ideal candidate will have hands-on experience with multi-agent architectures, LLM orchestration, and production-grade AI deployments. Qualifications - Strong programming skills in Python (preferred) or similar languages - Deep understanding of prompt engineering techniques and patterns - Experience working with OpenAI, open-source LLMs, or similar platforms - Familiarity with vector databases and embeddings (e.g., Pinecone, FAISS) - Knowledge of multi-agent architectures and orchestration strategies - Understanding of AI evaluation metrics and model performance tuning Requirements - Design, develop, and deploy applications powered by LLMs and generative AI models - Engineer, test, and optimize prompts for performance, accuracy, and reliability - Build and manage multi-agent systems for complex task orchestration - Integrate AI models with APIs, databases, and external tools - Implement retrieval-augmented generation (RAG) pipelines - Fine-tune and evaluate models using domain-specific datasets - Monitor model outputs, ensure alignment, and reduce hallucinations - Collaborate with product, data, and engineering teams to deliver AI-driven features Benefits - Experience with fine-tuning LLMs or training custom models - Knowledge of cloud platforms (AWS, GCP, Azure) - Exposure to conversational AI, chatbots, or copilots - Understanding of AI safety, guardrails, and governance