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About This Role
Where Ambition Meets Innovation
Build a career that matches all your initiative with an impressive dose of innovation. From cutting\-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.
Job Overview
The Principal Architect, AI is responsible for designing, building, and governing LPL's centralized AI Hub platform, enabling business domains to securely develop, deploy, and operate AI\-powered applications using a shared enterprise AI foundation.
This role defines the end\-to\-end enterprise AI platform architecture, including model access, agent orchestration, prompt and tool governance, data access, AI security, observability, compliance, and platform services. The architect partners with Enterprise Architecture, AI Engineering, Security, Infrastructure, Data, and Product teams to accelerate responsible, scalable, and compliant AI adoption across the organization.
Responsibilities
- Architect and lead LPL's Enterprise AI Hub Platform, providing centralized AI capabilities across business domains.
- Establish a shared AI platform for model access, agent orchestration, prompt management, tool integration, governance, and observability.
- Define reference architectures for Generative AI, Agentic AI, RAG, MCP, and Multi\-Agent solutions.
- Design and govern core platform services including:
+ Model, Agent, Prompt, and Tool Registries
+ LLM Gateway and AI Access Layer
+ MCP and A2A Gateways
+ Audit, Monitoring, and Compliance Services
- Design and govern the Data Access Layer for the AI platform, including secure and scalable access to structured and unstructured data sources, data abstraction and query interfaces, connection to vector databases and knowledge stores, data lineage, and enforcement of data governance, privacy, and permissioning policies across AI workloads.
- Lead experimentation and proof\-of\-concept (POC) efforts to evaluate emerging AI models, frameworks, and architectural patterns; run technical spikes to validate feasibility, performance, and scalability; and translate successful POCs into production\-ready platform capabilities.
- Lead implementation of AI Platform Engineering, MLOps, and LLMOps capabilities, including onboarding, deployment, monitoring, and lifecycle management.
- Define standards for agent onboarding, agent communication, AI interoperability, and model governance.
- Embed security\-by\-design principles including identity propagation, RBAC, PII protection, AI guardrails, content safety, and auditability.
- Ensure compliance with enterprise security, risk, regulatory, and responsible AI standards.
- Architect and govern AI observability, usage analytics, lineage, cost management, and compliance reporting.
- Evaluate and recommend enterprise technologies for foundation models, AI gateways, agent frameworks, MCP, vector databases, and AI governance platforms.
- Drive the transition from siloed AI solutions to a scalable, governed enterprise AI ecosystem.
- Collaborate with Enterprise Architecture, Engineering, Security, Data, and Product teams to align AI strategy with business objectives.
- Influence AI platform roadmaps, technology investments, governance frameworks, and long\-term AI strategy.
- Mentor architects, engineers, and AI teams on enterprise AI architecture and platform best practices.
What Are We Looking For?
We want strong collaborators who can deliver enterprise\-scale AI capabilities while balancing innovation, governance, security, and operational excellence. We are looking for individuals who thrive in a fast\-paced environment, are business\-focused and technology\-driven, and can execute in a way that promotes innovation, standardization, and responsible AI adoption.
Requirements
- 10\+ years of experience in Enterprise Architecture, Software Engineering, Platform Architecture, or Distributed Systems.
- 3\+ years of experience designing and implementing AI, Machine Learning, Generative AI, or Agentic AI platforms.
- 3\+ years experience architecting and implementing AI/ML systems and data access layers — not limited to high\-level design — including direct experience building or configuring platform services, integrations, and POCs.
- 5\+ years experience and deep understanding of cloud\-native architectures, APIs, microservices, event\-driven systems, platform engineering, and scalable distributed applications.
Core Competencies
- Ability to solve complex enterprise\-scale business and technology challenges.
- Deep expertise in AI platform architecture, data access architecture, cloud architecture, and enterprise integration patterns.
- Strong strategic thinking with the ability to balance immediate delivery needs and long\-term platform vision.
- Excellent verbal and written communication skills, capable of influencing technical and executive stakeholders.
- Strong leadership, mentoring, collaboration, and architecture governance capabilities.
- Passion for advancing scalable, secure, compliant, and enterprise\-grade AI adoption across the organization.
Preferences
- Strong expertise with AWS cloud services, Kubernetes/EKS, API Gateway, Bedrock, DynamoDB, security frameworks, and enterprise integration patterns.
- Experience designing and implementing data access layers, data abstraction patterns, and secure data integration for AI/ML workloads.
- Experience implementing AI governance, security, compliance, observability, and responsible AI controls.
- Strong knowledge of Generative AI, Agentic AI, MCP (Model Context Protocol), A2A (Agent\-to\-Agent), RAG, Vector Databases, and AI orchestration frameworks.
- Experience with MLOps, LLMOps, model registries, prompt management, deployment automation, model lifecycle management, and AI monitoring platforms.
- Demonstrated experience leading experimentation and proof\-of\-concept initiatives, evaluating new AI technologies, and driving them to production.
- Proficiency in Python, Java, APIs, containers, Infrastructure\-as\-Code, and modern DevSecOps practices.
- Proven ability to influence executive stakeholders, drive strategic initiatives, mentor teams, and lead cross\-functional architecture programs.
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
Pay Range:
$155,942\.00 \- $259,869\.00###
Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play – such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer! Company Overview:
LPL Financial Holdings Inc. (Nasdaq: LPLA) is among the fastest growing wealth management firms in the U.S. As a leader in the financial advisor\-mediated marketplace(6\) , LPL supports over 32,000 financial advisors and the wealth management practices of approximately 1,100 financial institutions, servicing and custodying approximately $2\.3 trillion in brokerage and advisory assets on behalf of approximately 8 million Americans. The firm provides a wide range of advisor affiliation models, investment solutions, fintech tools and practice management services, ensuring that advisors and institutions have the flexibility to choose the business model, services, and technology resources they need to run thriving businesses. For further information about LPL, please visit www.lpl.com.
At LPL, independence means that advisors and institution leaders have the freedom they deserve to choose the business model, services, and technology resources that allow them to run a thriving business. They have the flexibility to do business their way. And they have the freedom to manage their client relationships, because they know their clients best. Simply put, we take care of our advisors and institutions, so they can take care of their clients.
For further information about LPL, please visit www.lpl.com.
Join the LPL team and help us make a difference by turning life’s aspirations into financial realities. Please log in or create an account to apply to this position. Principals only. EOE.
Information on Interviews:
LPL will only communicate with a job applicant directly from an @lplfinancial.com email address and will never conduct an interview online or in a chatroom forum. During an interview, LPL will not request any form of payment from the applicant, or information regarding an applicant’s bank or credit card. Should you have any questions regarding the application process, please contact LPL’s Human Resources Solutions Center at (855\) 575\-6947\.
EAC 5\.19\.26
Salary Context
This $155K-$259K range is above the median for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At LPL Financial, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($207K) sits 12% below the category median. Disclosed range: $155K to $259K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LPL Financial AI Hiring
LPL Financial has 6 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in Fort Mill, SC, US. Compensation range: $167K - $273K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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