Principal AI Architect

LPL Financial

. Own and evolve reference architectures for AI-enabled applications, user-facing AI workspaces, agent runtimes, data stores, and supporting services .

Posted 11/10/2026full-timeUnited StatesLead💰 $155,942 - $259,869 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in AI architecture, including generative AI and agentic AI, with a strong focus on cloud-native design and enterprise integration. Proven ability to lead AI product lifecycles, ensuring governance, security, and operational excellence.

Highest-signal resume keywords

AI ArchitectureGenerative AICloud-Native DesignAI Product Lifecycle ManagementAWS Proficiency

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Hard Skills

PythonJavaMicroservicesAPIsEvent-Driven PatternsContainersKubernetesDevSecOpsArchitecture DocumentationMachine Learning

Soft Skills

MentoringCommunicationCollaboration

Tools & Technologies

AWS EKSAmazon BedrockAPI GatewayPostgreSQLDynamoDBS3Observability ToolingOpenAI-Compatible Inference APIs

Certifications & Qualifications

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

Industry Keywords

Financial ServicesRegulated DomainsEnterprise Identity PlatformsDelegated Authorization

Tech Stack

Tools & technologies

AssemblyAWSCloudDistributed SystemsDynamoDBJavaKubernetesMicroservicesPostgresPython

About the role

Key responsibilities & impact

Own and evolve reference architectures for AI-enabled applications, user-facing AI workspaces, agent runtimes, data stores, and supporting services
Define patterns for durable conversational and non-conversational user work, memory, artifact continuity, degradation, and web-platform integration
Architect intent routing, context assembly, agent and domain-service delegation, human-in-the-loop controls, and advisory-only boundaries
Define experience architecture with product and UX partners, including structured responses, latency budgets, streaming, error recovery, and multi-intent interactions
Partner on availability, deployment strategy, capacity, performance testing, and disaster recovery
Define governed enterprise AI integration through APIs/SDKs, 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 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 development lifecycle from use-case intake through certification, change management, review, and retirement
Define classification, risk-tiering, evidence, evaluation, approval, and identity requirements
Establish production readiness gates, 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/event review, security, risk, and compliance forums
Define security architecture covering SSO, token flows, delegated authorization, least-privilege 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 telemetry-driven improvement
Define reference architectures for generative AI, agentic AI, RAG, tool-enabled and multi-agent solutions, and MCP/A2A interoperability
Run technology radar POCs and transition successful technologies into reference implementations and roadmap input
Mentor solution and software architects, influence roadmaps, and communicate tradeoffs to 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
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
Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field is preferred

Benefits

Comp & perks

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