Jobs at RedpineAI

Design and build cloud infrastructure, data platforms, and APIs that deliver licensed, high-quality data to AI models and agents in real time. Own architecture, data access abstractions, retrieval, latency, reliability, performance, observability, and global scalability for a new AI data platform.

Own Redpine’s knowledge graph end to end, including ontology and schema design, entity resolution, provenance, confidence and conflict handling, source-change propagation, and graph-powered retrieval. Build production data pipelines from multimodal sources and enable multi-hop, graph-and-vector retrieval for AI agents. This high-ownership role requires strong Python, direct knowledge graph experience, and practical expertise with retrieval and RAG systems.

Develop and scale AI/ML solutions for data quality assessment, automated labeling verification, data processing, matching, and evaluation. Build scalable pipelines and deploy models using modern serving, containerization, and orchestration tools. Collaborate with product teams, customers, and stakeholders to define data requirements and marketplace features, while researching state-of-the-art techniques for improving licensed AI training and inference data.

Drive Redpine’s expansion into new markets and client segments by identifying, developing, negotiating, and closing partnerships with AI companies, data suppliers, customers, and strategic collaborators. Lead international commercial efforts, deliver technical product demonstrations, advise partners on AI-native data strategies, and collaborate with legal, product, and partnerships teams on data licensing opportunities.

Work directly with founders at an AI startup to source and close data partnerships: identify decision-makers, run outreach, progress deals end-to-end, research global data markets, and support strategic ad-hoc projects.

Lead the design for web and product experiences, defining visual systems, collaborating with teams, and advocating for user-centered design.

Lead and build an AI-native engineering organization: own architecture (data pipelines, retrieval, reranking, knowledge graph, APIs/CLI), coach and grow engineers, set delivery cadence, balance reliability/latency/cost, and translate frontier AI research into production features.