Principal Data / AI Architect

Principal Data / AI Architect Principal Data / AI Architect A bit about this role: Brightside is seeking a Principal Architect, Data & AI Platforms, to own and evolve the technical foundations that power our AI-driven experiences, platform integrations, and analytics. This is a senior, hands-on architecture role designed for someone who combines deep data architecture expertise, strong applied AI judgment, and the ability to translate business strategy into durable technical systems. This role reports directly to the CTO and serves as the CTO’s right-hand partner for data, AI, and platform architecture decisions. You will have clear decision authority over data and AI architecture across engineering teams, while partnering closely with Product, Analytics, and Engineering leadership to ensure speed, quality, and long-term integrity. This is not a research role, nor a people-management role. It is an execution-oriented architecture leadership role focused on clarity, leverage, and outcomes. The meaningful work you will tackle: Enterprise Data & AI Architecture Own the data and AI architecture across the company, spanning: AI-powered systems and decisioning workflows Core application platforms Analytics and reporting layers Define and evolve canonical data models across clients, employers, partners, financial products, and outcomes Ensure consistency across transactional systems, analytical platforms, and AI feature layers Data Strategy & Integration Leverage Act as the company’s data strategy expert, with deep understanding of: Employer integrations (eligibility, payroll, SSO, identity) Partner integrations and product data External and enrichment data sources (e.g., credit, public datasets) Identify which data sources meaningfully compound business and product value, and which do not Guide integration and platform investments based on data leverage and long-term value, not just feature demand AI System Design & Governance Design and govern production-grade AI systems, including: LLM-based applications (RAG, prompt orchestration, embeddings, vector stores) Decisioning and automation workflows Evaluate and govern AI models and platforms (e.g., OpenAI, Anthropic, open-source), balancing: Accuracy and reliability Cost and latency Security, privacy, and explainability Define standards for AI lifecycle management (build, deploy, monitor, iterate, retire) Architecture Authority & Decision-Making Act as the decision-maker for data and AI architecture decisions across engineering teams Partner with the Enterprise Architecture Board to review proposals, surface risks, and ensure coherence Establish clear architecture standards and review processes that enable teams to move fast without creating long-term risk or technical debt Prototyping & Technical Discovery Lead rapid prototyping and technical discovery to: Test architectural assumptions Evaluate new AI approaches Inform investment and roadmap decisions Personally build or lead proofs-of-concept where needed to drive alignment and reduce uncertainty Executive & Cross-Functional Partnership Partner closely with the CTO, Product leaders, Analytics, and Engineering to: Translate business strategy into technical direction Align data, AI, and platform investments Serve as a trusted technical advisor to executive leadership on data, AI, and platform tradeoffs How You’ll Work Hands-on, opinionated, and pragmatic Focused on clarity over complexity Comfortable saying “no” to poor architectural decisions - and explaining why Oriented toward production outcomes, not theoretical elegance Balancing experimentation with rigor in a regulated fintech environment What we’re looking for in your background & what makes you a success: 10+ years of experience in software engineering, data architecture, or systems architecture, with significant hands-on experience Deep expertise in data architecture and data modeling, including relational databases, event-driven systems, and analytical data platforms Strong experience designing and operating data platforms at scale, including data lakes/warehouses and real-time pipelines Strong experience designing and operating AI/ML systems in production, including LLM-based architectures (RAG, embeddings, vector databases, prompt orchestration) Proven experience in regulated environments (fintech, financial services, healthcare, etc.), with an understanding of data privacy, security, and compliance