Senior Applied AI Systems Architect (Industrial Commerce)

Working Style & Expectations

We are looking for committed, highly motivated professionals who take ownership seriously. This role requires consistent collaboration during U.S. Eastern Time business hours, reliability in scheduling, and strong follow-through. Being on time, prepared, and diligent is essential — this is a senior role on a system that underpins real operations, not an experimental project.

We value people who are excited by hard problems, communicate clearly, and treat commitments as non-negotiable.

1. AI/ML Developer Model Training & Fine Tuning

2. Data Model & Architect

3. Developers Microservices / API  ETL & Integration developement

We are building an end-to-end system that turns unknown physical parts into known, sellable products — automatically. The system ingests parts through vision and sensors, resolves identity using AI and structured knowledge, and publishes those parts into a fully integrated commerce, logistics, and search ecosystem.

This is not a research role. It is a production systems role spanning computer vision, large-scale data ingestion, retrieval systems, cloud infrastructure, and e-commerce integration.

What you will build

• A learning ingestion pipeline that identifies unknown physical parts using images, 3D data, and metadata, and improves continuously through human confirmation
• Vision and retrieval systems using multimodal embeddings and vector search to match parts across large catalogs
• A RAG-based knowledge system that ingests, parses, and normalizes manufacturer PDFs, schematics, and parts manuals into APIs
• Event-driven services that publish identified parts into inventory, commerce, shipping, and ERP workflows
• Search-optimized part representations engineered to be discoverable by Google, analytics systems, and downstream AI models

Why this is hard — and interesting

• Parts are visually similar, worn, incomplete, and inconsistent across manufacturers
• Manufacturer data is fragmented, unstructured, and often only available as PDFs and diagrams
• The system must operate across perception, inference, search, commerce, and logistics — not just ML
• Identification must be probabilistic, explainable, and correctable, while downstream systems require deterministic truth
• The platform must learn continuously without breaking accounting, inventory, or customer trust

This is a real-world intelligence problem, not a model-training exercise.

Technical environment

• Google Cloud Platform (Cloud Run, Compute Engine, GPU instances, Cloud Storage)
• MongoDB Atlas for operational and catalog data
• Vector search and retrieval systems for embeddings and similarity matching
• Event-driven and API-first architecture
• Computer vision, multimodal embeddings, and RAG pipelines in production

Required experience

• Senior-level experience building production AI or data-driven systems end-to-end
• Strong background in computer vision, retrieval systems, or multimodal ML
• Experience designing cloud-native systems on GCP or similar platforms
• Deep Python expertise; comfort owning architecture and trade-offs
• Ability to connect ML outputs to real business systems (commerce, ERP, logistics)

What success looks like

• Unknown parts can be ingested, identified, and published without manual data entry
• Manufacturer documents become structured, queryable knowledge
• Identified parts flow cleanly into e-commerce, search, and fulfillment systems
• The system improves over time without increasing operational risk