We are looking for a Data & AI Architect to lead the design of modern data platforms and intelligent systems.. This is a fully remote opportunity that blends enterprise data architecture with applied AI, focusing on scalable platforms, retrieval-driven solutions, and production-grade agentic workflows. The ideal candidate will shape technical standards, guide platform decisions, and contribute hands-on expertise to build secure, high-performing solutions that support advanced analytics and AI adoption.

Responsibilities:

• Establish enterprise data models, shared business semantics, and data interface standards that support secure, tenant-aware access across relational, document, key-value, object, graph, vector, and analytical data platforms.
• Design cloud-based data ecosystems, including warehouses and lakehouses, and select the right technologies based on throughput, latency, scale, and cost requirements.
• Create architecture for batch and streaming pipelines, transformation layers, and data-serving systems with strong attention to lineage, recoverability, freshness, data quality, and operational accountability.
• Lead the design of embedding pipelines, vector lifecycle management, semantic retrieval, hybrid search, reranking strategies, and evaluation methods for high-quality search experiences.
• Architect knowledge-driven and graph-aware solutions that improve entity resolution, recommendation quality, matching accuracy, and personalization outcomes.
• Define production-ready approaches for retrieval-augmented AI and single- or multi-agent systems, including context design, citation grounding, orchestration patterns, state handling, approval workflows, and resilient failure recovery.
• Establish measurement and observability practices for AI systems by tracking quality, safety, reliability, cost, latency, and task performance through traceable debugging and regression testing.
• Build, review, and optimize critical data pipelines, AI prototypes, retrieval services, and evaluation frameworks to support real-world production use cases.
• Implement repeatable engineering practices such as automated testing, CI/CD, documentation, monitoring, and recovery procedures that enable reliable long-term operation.
• Set architectural direction for governance, privacy, authorization, retention, auditability, and AI safety controls while advising on platform strategy, vendor evaluation, and technical decision-making.