Principal AI Architect

LPL Financial•Fort Mill, SC

22h•$155,942 - $259,869

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 end-to-end AI product development lifecycle, from discovery and experimentation through design, delivery, evaluation, certification, operations, change management, and retirement. The architect establishes repeatable governance and engineering patterns that let product teams use a common, governed path. The position combines strategic architecture with hands-on technical work, including 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.

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 owning AI product lifecycle outcomes spanning governance, security, evaluation, and operations in addition to models.
2+ years of hands-on experience with AI applications, agents, RAG, APIs, integrations, and technical proofs of concept, with the ability to review code and designs alongside 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 another highly regulated domain, 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, along with strong architecture documentation skills such as 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, including user-facing AI workspaces and their agent runtime, 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.
Architect intent routing, context assembly, and delegation between agents and domain services, with human-in-the-loop controls and advisory-only boundaries.
Define experience architecture with product and UX partners, including response rendering, latency budgets, streaming, error recovery, and multi-intent interactions.
Partner on non-functional requirements such as availability, deployment strategy, capacity, performance testing, and disaster recovery.
Define how AI products integrate with a governed perimeter, including APIs and SDKs, AI gateway routing, policy enforcement, managed 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 for 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.
Lead or co-own architecture for a repeatable AI lifecycle from use-case intake through production certification, change and exception management, periodic review, and retirement.
Define classification and risk-tiering models to determine required evidence, evaluations, approvals, and identity steps.
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 AI security architecture, including SSO and token flows, scoped agent identity, delegated authorization, gateway validation, least-privilege tool access, PII protection, content safety, and records retention.
Set standards for evaluations of groundedness, hallucination, safety, agent behavior, context and memory, semantic retrieval, and enterprise data access.
Lead LLMOps and AgentOps practices covering versioning, deployment, monitoring, release management, FinOps, and telemetry-driven continuous improvement.
Define reference architectures for generative AI, agentic AI, RAG, tool-enabled and multi-agent solutions, and MCP/A2A interoperability.
Run technology radar activities, conduct proofs of concept for models and AI tooling, and transition successful efforts into reference implementations and roadmap input.
Mentor solution and software architects, influence roadmaps, and communicate tradeoffs to senior product, engineering, and risk stakeholders.