SENIOR AI ENGINEER / SYSTEMS ARCHITECT – CPGIO

About CPGIO: CPGIO is revolutionizing commerce intelligence with a $100M/year bootstrapped business and a track record supporting over 500 iconic brands. As we evolve from elite eCommerce operations into an AI-driven intelligence platform, we’re building tools that fuse reasoning agents, big data, and autonomous workflows to drive smarter, faster decisions for brand managers and operators.
We’re not just engineering infrastructure—we’re building intelligent systems that think, reason, and act.

IDEAL EXPERIENCE & SKILLS:

• LLM Development & Prompt Engineering: Hands-on with OpenAI/Anthropic/Gemini APIs.
• RAG/CAG/MCP Proficiency: Experience chaining models, augmenting with retrieval, and building reusable reasoning pipelines.
• Big Data Understanding: Capable of working with large datasets, especially using PostgreSQL, Supabase, or Neon.
• Visualization & Communication: Comfortable building interfaces or insights with Three.js, D3, or similar tools.
• Tooling Stack: Node.js, Python, Express, GitHub Actions, Cursor, Windsurf.
• Probabilities & Stats: Solid grounding in statistics, Bayesian reasoning, and performance tuning.
• DevOps Optional: CI/CD pipelines, observability (Grafana, Loki, Mimir), containerized deployments a plus.

ROLE OVERVIEW:

We’re looking for an AI-obsessed engineer with real-world experience building with LLM APIs (OpenAI, Anthropic, Gemini), chaining models (MCP), developing context-aware agents, and designing AI-native systems using Retrieval-Augmented Generation (RAG), Context-Aware Generation (CAG), and operator-driven architectures. If you’ve been pushing the boundaries of prompt engineering, mapping large datasets to actionable outcomes, or developing novel agentic workflows—this is your dream role.
You’ll join a tight-knit team reporting directly to the Chief Product & Technology Officer, building the foundational systems that will power the next wave of intelligent commerce.

WHAT YOU’LL DO: AGENT & AI SYSTEM ARCHITECTURE

• Design and implement reasoning agents and pipelines using RAG, CAG, and MCP (Model Chaining Pipelines).
• Develop operators and workflows that convert messy data into context-rich actions.
• Build LLM-native applications with OpenAI, Claude (Anthropic), and Gemini (Google).