• Drive AI innovations that make high quality mental healthcare more accessible. • Be the founding member of our AI technical team, defining and executing its roadmap. • Lead cross-functional efforts spanning engineering, clinical, compliance, and product teams. • Define and lead the AI roadmap across experimentation, model evaluation, deployment, and iteration. • Architect systems to support safe and scalable LLM interactions—including prompt chaining, memory retention, and RAG pipelines. • Develop strategies for model selection and cost-effective inference at scale. • Design and implement experiments to evaluate the efficacy, safety, and impact of AI innovations. • Build and scale a high-performing AI team across machine learning, data science, MLOps, and backend infrastructure. • Serve as the AI technical authority in conversations with external vendors, cloud partners, compliance officers, and executive stakeholders. • Partner with legal and clinical teams to ensure HIPAA compliance, BAA alignment, and safe handling of sensitive patient data.

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 requirements - Strong in modern cloud architectures (e.g., AWS) and modern data stacks (e.g., Databricks) - Ability to connect technical decisions to business outcomes, including cost efficiency, scalability, risk mitigation, and customer experience - Strong communication skills and comfort influencing across engineering, product, and executive leadership - Experience serving as a Lead Architect in a high-growth or transformation-stage company - Background in financial systems, payments, lending, or financial data platforms - Experience with AI governance, model risk management, or explainability frameworks - Track record of improving engineer productivity through platform and architecture design

• Apply AI/ML modeling techniques in solving many types of problems • Develop and design algorithmic solutions for automation projects • Design, develop, and implement data science solutions to address defense-related challenges • Utilize advanced coding skills to manipulate, analyze, and visualize large and complex datasets • Develop and deploy machine learning models and algorithms to extract insights and make predictions • Collaborate with cross-functional teams to identify data requirements, gather relevant data, and define project objectives • Design and optimize data architecture and infrastructure to support efficient data storage, processing, and analysis • Stay up-to-date with the latest trends and advancements in data science, machine learning, and defense technology • Communicate findings and recommendations to technical and non-technical stakeholders through clear and compelling presentations and reports

• Design, develop, and deploy scalable AI/ML solutions for real-world healthcare and operational use cases. • Own end-to-end ML pipelines: data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. • Develop and optimize automation systems (e.g., intelligent customer email order processing). • Build and improve conversational AI and voice agents for sales and support teams. • Contribute to computer vision initiatives, including automation projects in logistics. • Translate complex business requirements into well-structured technical solutions. • Ensure performance, scalability, monitoring, and reliability of models in production. • Collaborate cross-functionally with product, business, and engineering stakeholders. • Contribute to technical discussions, innovation sessions, and continuous improvement initiatives.