Principal AI Architect

LPL Financial

. Define and drive architecture for the enterprise AI portfolio, including advisor-facing AI workspaces, a centralized AI control plane, and agentic solutions .

Posted 11/9/2026full-timeUnited StatesLead💰 $155,942 - $259,869 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in enterprise AI architecture, including governance, security, and lifecycle management of AI products. Proficient in cloud-native design and hands-on experience with AI applications and integrations.

Highest-signal resume keywords

Enterprise ArchitectureGenerative AI ArchitectureCloud-Native DesignAI Product Lifecycle ManagementPython Programming

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Hard Skills

Distributed SystemsMicroservicesAPIsEvent-Driven PatternsContainersKubernetesDevSecOpsArchitecture DocumentationAI ApplicationsMachine Learning Platforms

Soft Skills

MentoringCommunicationInfluencing Roadmaps

Tools & Technologies

AWSEKSAmazon BedrockAPI GatewayPostgreSQLDynamoDBS3Observability ToolingOpenAI-Compatible Inference APIsMCP/A2A

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree in Engineering or AI

Industry Keywords

Financial ServicesHighly Regulated DomainsEnterprise Identity PlatformsNon-Human IdentityDelegated Authorization

Tech Stack

Tools & technologies

AssemblyAWSCloudDistributed SystemsDynamoDBJavaKubernetesMicroservicesPostgresPython

About the role

Key responsibilities & impact

Define and drive architecture for the enterprise AI portfolio, including advisor-facing AI workspaces, a centralized AI control plane, and agentic solutions
Provide architecture leadership across the AI product development lifecycle from discovery and intake through experimentation, design, build, evaluation, certification, deployment, operations, change management, and retirement
Establish repeatable governance and engineering patterns for secure, resilient, observable, and cost-aware AI at scale
Own and evolve reference architectures for AI-enabled applications, user-facing AI workspaces, agent runtimes, data stores, and supporting services
Define patterns for sessions, memory, artifact continuity, graceful degradation, and integration with identity, entitlements, and feature flags
Architect intent routing, context assembly, delegation between agents and domain services, human-in-the-loop controls, and advisory-only boundaries
Define 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
Define governed AI integration through APIs/SDKs, gateways, 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 execution, rate limiting, pause/quarantine/kill mechanisms, and incident evidence capture
Specify reusable platform services for memory, knowledge and RAG, evaluation, and observability
Lead or co-own the AI product lifecycle, governance, classification, risk-tiering, production readiness gates, and exception management
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
Define security architecture covering SSO, token flows, delegated authorization, least-privilege tool access, PII protection, content safety, and records retention
Set evaluation standards for groundedness, hallucination, safety, agent behavior, context, memory, retrieval, and enterprise data access
Lead LLMOps and AgentOps, including versioning, deployment, monitoring, release management, FinOps, and 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, proofs of concept, and reference implementations
Mentor solution and software architects, influence roadmaps, and communicate tradeoffs to senior product, engineering, and risk stakeholders

Requirements

What you’ll need

10+ years in enterprise, platform, or software architecture (distributed systems, cloud-native, data-intensive systems)
2+ years architecting generative AI, agentic AI, or ML platforms from experiment through regulated or enterprise production
2+ years demonstrated ownership of AI product lifecycle outcomes, spanning governance, security, evaluation, and operations in addition to models
2+ years hands-on credibility with AI applications, agents, RAG, APIs, integrations, and technical POCs
Ability to review code and designs with engineering teams
7+ years strong command of cloud-native design: microservices, APIs, event-driven patterns, containers/Kubernetes, IAM, observability, resilience, and performance
Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field (preferred qualification)
Depth in AWS: EKS, Amazon Bedrock, API Gateway, IAM, PostgreSQL, DynamoDB, S3, and observability tooling
Experience with AI gateways and OpenAI-compatible inference APIs
Familiarity with tool calling, MCP, A2A, multi-agent orchestration, embeddings, and vector search
Experience in financial services or other highly regulated domains
Knowledge of enterprise identity platforms, non-human identity (NHI) for agents, and delegated authorization patterns
Proficiency in Python, Java, IaC, CI/CD, and DevSecOps
Strong architecture documentation skills, including C4, sequence and data-flow diagrams, and ADRs

Benefits

Comp & perks

401K matching
Health benefits
Employee stock options
Paid time off
Volunteer time off
Competitive LPL Total Rewards package