Solutions Architect

Interval helps enterprises turn messy, underused data into governed, high-confidence intelligence—without handing control to a black box. We bring compute to your data with a private data lakehouse, verifiable audit trails, and U-AI, our contextual AI framework for secure AI workflows.

• →Architect end-to-end solutions that integrate Interval’s data-exchange, privacy, and AI capabilities with customers’ existing cloud warehouses, lakehouses, and BI stacks.
• →Lead technical discovery & design workshops with data, analytics, and ML teams; produce reference architectures, sequence diagrams, and implementation plans.
• →Prototype & run POCs showcasing secure LLM workflows, private-model training, and governed data-sharing on Interval’s EVM-backed network.
• →Serve as a trusted advisor to enterprise stakeholders—CTOs, CDOs, and Heads of AI—on topics such as data-governance, compliance (HIPAA, GDPR), and ML-ops best practices.
• →Collaborate cross-functionally with Product, Engineering, and Sales to influence roadmap priorities and ensure customer success.
• →Create reusable assets and enablement content that scale architectural best practices across the organization.
• →Evangelize Interval through conference talks, blog posts, and community contributions to the modern data stack and open-source AI tooling.

• 8 + years designing and delivering enterprise SaaS or cloud data solutions, with recent senior-level ownership of architecture or presales design.
• Deep hands-on expertise with at least one of Databricks, Snowflake, GCP, AWS or Salesforce Data Cloud.
• Working knowledge of machine-learning & LLM workflows (vector DBs, embedding pipelines, fine-tuning, RLHF) and the surrounding ML-Ops toolchain.
• Fluency in SQL and one scripting language (Python preferred); ability to read infrastructure-as-code
• Strong grasp of modern data-governance, security, and compliance patterns (encryption, IAM, row/column-level security).
• Exceptional written & verbal communication skills—you can whiteboard complex systems and influence both executive and engineering audiences.

• Experience with blockchain-based data protocols, tokenization, or zero-knowledge proofs.
• Prior success building reference architectures or contributing to OSS projects in the data/AI ecosystem.
• Domain exposure in high-signal industries (finance, energy, real estate, logistics) where data provenance and policy enforcement are mission-critical.