Principal AI Architect

About the role

We are looking for a Principal AI Architect to own the design and build of Brightside’s AI agent platform. This is a senior, hands-on role and you will work directly alongside the CTO and engineering team to architect and ship production systems, not design from a distance.

Responsibilities

• Real-Time Agent Architecture Design and own the multi-agent orchestration layer - session supervisor, domain clusters, escalation arbiter, and conversation manager
• Define agent routing logic, context sharing, tool use, and latency budgets across 18+ agents
• Architect the real-time pipeline: Amazon Connect → Contact Lens → Kinesis → Lambda → Bedrock → WebSocket push to FA panel
• Own the dual-mode agent architecture: human-assisted mode (agent coaches the FA) and autonomous mode (agent conducts the client conversation directly)
• LLM Systems & Prompt Engineering Governance
• Define model selection and routing across low-latency and reasoning-intensive agent tiers
• Govern the prompt architecture: structured agent prompt standards, runtime domain knowledge injection, and conditional output formatting across delivery surfaces
• Build and own the eval harness - schema validation, behavioral regression testing, latency profiling
• Design the domain knowledge base structure across the full financial use case library - 8 domains, 49 use case types
• Data Architecture & CRM Integration
• Own the data model underlying the AI platform: client financial profiles, cases, goals, options, outcomes, and financial impact measurement
• Architect the CRM sync layer - real-time field writes after FA confirmation, human correction logging as training signal
• Define canonical data models across clients, employers, financial products, and outcomes
• Ensure data integrity across Aurora PostgreSQL (new CRM) and Aurora MySQL (existing platform)
• AI Infrastructure & Platform
• Own the AWS Bedrock-native inference infrastructure — async invocations, Knowledge Bases, RAG pipelines
• Design the session state layer: ElastiCache Redis for hot state, DynamoDB for durable session records
• Define AI governance standards: model evaluation, monitoring, explainability, and compliance in a regulated fintech environment
• Evaluate and guide decisions on model providers, orchestration frameworks, and platform tooling
• Engineering Partnership
• Serve as the CTO’s right-hand technical partner on all AI and platform architecture decisions
• Work alongside the Engineering Lead and team as a co-owner of the architecture - present in design, implementation, and debugging
• Translate business requirements from Client Services and Product into architecture-aligned technical scope
• Lead prototyping and technical discovery to test assumptions and reduce uncertainty before committing investment
• 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

Qualifications

• 10+ years in software engineering or systems architecture, with significant hands-on experience in the last 3 years
• Production experience building real-time AI systems - specifically systems that operate on a per-turn, sub-second latency budget
• Hands-on experience with Amazon Bedrock, AWS Lambda, and Kinesis — not just familiarity, but have hit their limits and worked around them
• Multi-agent system design experience - orchestration patterns, agent handoff, context sharing, structured output validation
• Strong data architecture foundation - relational modeling, event-driven systems, CRM data patterns
• Experience in a regulated environment (fintech, financial services, or healthcare) with an understanding of privacy and compliance requirements
• Comfortable operating in a small, fast-moving team where you design and build - not just review and approve

What makes you a success

• Experience building voice or conversation intelligence systems (contact center AI, real-time transcription pipelines, live agent coaching tools)
• Background in conversational AI product companies (Cresta, Observe.AI, Cogito, Replicant, or similar)
• Experience with LLM prompt engineering at a systems level - eval harnesses, versioned prompt governance, regression testing
• Prior experience as a lead architect at a Series B–D company where you were one of a small number of senior technical voices