Principal AI Architect

LPL Financial•Charlotte, TX

10h

About The Position

The Principal AI Architect defines and drives architecture for LPL Financial’s enterprise AI portfolio, including advisor-facing AI workspaces, a centralized AI control plane, and agentic solutions. The role provides architecture leadership across the AI product development lifecycle, from discovery and experimentation through design, build, evaluation, certification, deployment, operations, change management, and retirement. It establishes repeatable governance and engineering patterns so teams can use a common, governed path while delivering secure, resilient, observable, and cost-aware AI at scale in a regulated financial services environment. The role combines strategic architecture with hands-on technical depth: evaluating emerging models and frameworks, building reference implementations and proofs of concept, and guiding product and engineering teams. Focus areas will be calibrated to the successful candidate’s strengths and organizational priorities. The architect owns and evolves reference architectures for AI applications, workspaces, agent runtimes, and supporting services. They define patterns for durable user work, intent routing, context assembly, agent delegation, human-in-the-loop controls, experience architecture, and non-functional requirements. They also define how AI products integrate with a governed enterprise perimeter, including gateways, policy enforcement, managed inference, audit logging, cost attribution, model and agent registries, runtime controls, and reusable platform services. The role leads or co-owns a repeatable AI product lifecycle and governance approach, including classification and risk tiering, production readiness gates, evaluation and safety standards, release evidence, operational runbooks, and onboarding guidance. It aligns AI architecture with enterprise architecture, API and event review, security, risk, and compliance forums. The architect defines security architecture and standards for evaluation, context, memory, retrieval, and enterprise data access. They lead LLMOps and AgentOps practices such as versioning, deployment, monitoring, release management, cost governance, and continuous improvement. They also conduct technology radar activities, mentor architects, influence roadmaps, and communicate tradeoffs to senior product, engineering, and risk stakeholders.

Requirements

10+ years in enterprise, platform, or software architecture, including distributed systems, cloud-native, or data-intensive systems.
2+ years architecting generative AI, agentic AI, or ML platforms from experimentation through regulated or enterprise production.
2+ years of demonstrated ownership of AI product lifecycle outcomes spanning governance, security, evaluation, and operations in addition to models.
2+ years of hands-on credibility with AI applications, agents, RAG, APIs, integrations, and technical proofs of concept, with the ability to review code and designs with engineering teams.
7+ years of strong command of cloud-native design, including microservices, APIs, event-driven patterns, containers/Kubernetes, IAM, observability, resilience, and performance.

Nice To Haves

Depth in AWS, including EKS, Amazon Bedrock, API Gateway, IAM, PostgreSQL, DynamoDB, S3, and observability tooling.
Experience with AI gateways and OpenAI-compatible inference APIs.
Familiarity with agent protocols and frameworks, including tool calling, MCP, A2A, multi-agent orchestration, embeddings, and vector search.
Experience in financial services or other highly regulated domains, including supervisory records, advice-related content controls, and entitlement-heavy systems.
Knowledge of enterprise identity platforms, non-human identity for agents, and delegated authorization patterns.
Proficiency in Python, Java, infrastructure as code, CI/CD, and DevSecOps.
Strong architecture documentation skills, including C4, sequence and data-flow diagrams, and ADRs.
Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field.

Responsibilities

Own and evolve reference architectures for AI-enabled applications, advisor-facing AI workspaces, agent runtimes, data stores, and supporting services.
Define patterns for durable user work, including sessions, memory and artifact continuity, graceful degradation, and integration with existing web platforms and their identity, entitlement, and feature-flag systems.
Architect intent routing, context assembly, and delegation between agents and domain services, including human-in-the-loop controls and advisory-only boundaries.
Define AI experience architecture with product and UX partners, including structured response rendering, latency budgets, streaming, error recovery, and multi-intent interactions.
Partner on availability, deployment strategy, capacity, performance testing, and disaster recovery requirements from pilot through scale-out.
Define how AI products integrate with a governed enterprise perimeter, including APIs and SDKs, AI gateway routing, policy enforcement, managed model inference, audit logging, and cost attribution.
Architect registries and lifecycle patterns for models, prompts, tools, and agents, including version promotion and kill-switch capability.
Establish runtime controls such as entitlements, governed tool and agent execution, rate limiting, pause and quarantine mechanisms, kill mechanisms, and incident evidence capture.
Specify reusable platform services such as memory, knowledge and RAG abstractions, evaluation hooks, and observability contracts.
Lead or co-own a repeatable AI product lifecycle from use-case intake through production certification, ongoing change and exception management, periodic review, and retirement.
Define classification and risk-tiering models that determine required evidence, evaluations, approvals, and identity steps for each solution type.
Establish production readiness gates, including offline and runtime evaluations, regression suites, red-team and safety standards, release evidence, and operational runbooks.
Publish onboarding playbooks for internal teams and approved third parties.
Align AI architecture with Enterprise Architecture, API and event review, security, risk, and compliance forums, and represent AI designs in architecture reviews.
Define security architecture for AI solutions, including SSO and token flows, scoped agent identity and delegated authorization, gateway validation, least-privilege tool access, PII protection at egress, content safety, and records retention.
Set standards for evaluation of groundedness, hallucination, safety, and agent behavior, as well as context and memory, semantic retrieval, and enterprise data access.
Lead LLMOps and AgentOps, including versioning, deployment, monitoring, release management, FinOps, and continuous improvement using production telemetry.
Define reference architectures for generative AI, agentic AI, RAG, tool-enabled and multi-agent solutions, and MCP/A2A interoperability.
Run technology radar activities and proofs of concept for models, agent frameworks, gateways, and evaluation tooling; transition successful approaches into reference implementations and platform roadmap input.
Mentor solution and software architects, influence roadmaps, and communicate tradeoffs to senior product, engineering, and risk stakeholders.