Where Ambition Meets Innovation
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Job Overview
The Principal Architect, Artificial Intelligence defines and drives architecture for our enterprise AI portfolio, including advisor-facing AI workspaces, a centralized AI control plane, and the agentic solutions built on top of both. This role provides architecture leadership across the end-to-end AI product development lifecycle (AI PDLC): discovery and intake, experimentation, design, build, evaluation, certification, deployment, operations, change management, and retirement.
You will establish repeatable governance and engineering patterns so that product teams can onboard to a common, governed path without reinventing controls for each initiative. The role pairs strategic architecture with hands-on depth: evaluating emerging models and frameworks, building reference implementations and proofs of concept, and guiding teams to deliver secure, resilient, observable, and cost-aware AI at scale in a regulated financial services environment.
Focus areas will be calibrated to the successful candidate’s strengths and organizational priorities.
Key Responsibilities:
AI Solution and Platform Architecture
Own and evolve reference architectures for AI-enabled applications, including user-facing AI workspaces and the agent runtime, data stores, and services behind them.
Define patterns for durable user work: conversational and non-conversational sessions, memory and artifact continuity, graceful degradation, and integration into existing web platforms (identity, entitlements, feature flags).
Architect intent routing, context assembly, and delegation between agents and domain services (A2A and task models), including human-in-the-loop controls and advisory-only boundaries so no unintended automated actions occur.
Define experience architecture with product and UX partners: structured response rendering, latency budgets, streaming, error recovery, and multi-intent interactions.
Partner on non-functional requirements: availability, deployment strategy, capacity, performance testing, and disaster recovery from pilot through scale-out.
Enterprise AI Control Plane and Integration
Define how AI products conform to a governed perimeter: API/SDK integration, AI gateway routing, policy enforcement, managed model inference, audit logging, and cost attribution.
Architect registry and lifecycle patterns for models, prompts, tools (including MCP), and agents (e.g., Draft → Active → Suspended → Retired), including version promotion and kill-switch capability.
Establish runtime controls: entitlements, governed tool and agent execution, rate limiting, pause/quarantine/kill mechanisms, and incident evidence capture.
Specify reusable platform services, such as memory, knowledge and RAG abstractions, evaluation hooks, and observability contracts, that reduce duplication across teams.
AI Product Development Lifecycle and Governance
Lead or co-own the architecture of a repeatable path from use-case intake through production certification, ongoing change and exception management, periodic review, and retirement.
Define classification and risk-tiering models that determine the evidence, evaluations, approvals, and identity steps required for each solution type.
Establish production readiness gates: offline and runtime evaluation requirements, regression suites, red-team and safety standards, release evidence, and operational runbooks.
Publish onboarding playbooks for internal teams and approved third parties, replacing one-off project checklists with consistent guidance.
Align AI architecture with Enterprise Architecture, API and event review, security, risk, and compliance forums, and represent AI…