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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:
We are seeking an experienced Identity and Access Management (IAM) Architect with a strong AI and agent-integration focus to lead the design, proof-of-concept (POC), and hands-on implementation of identity patterns for AI workloads, conversational agents, and AI platform integrations across the enterprise. The ideal candidate combines deep IAM architecture expertise with practical engineering skills—building POCs, configuring OAuth/OIDC flows, and partnering directly with AI engineering teams to secure agent runtimes, tool access, and human-in-the-loop experiences.
This role owns IAM architecture for AI use cases, including delegated and service-to-service access, API gateway/BFF token flows, scoped credentials, and governance alignment. You will design and validate OAuth/OIDC patterns (Auth Code + PKCE, OBO, token exchange, client credentials) across identity providers (PingOne AIC, Entra ID), gateways, and agent platforms. The AI IAM Architect partners across AI/platform engineering, IAM, security, and enterprise architecture to define reusable, secure, and production-ready identity standards for agents.
Key Responsibilities:
- Discover AI/agent identity requirements across users, services, runtimes, tools, and APIs.
- Assess existing SSO, MFA, federation, and API authorization models; identify gaps in delegation, token lifecycle, scopes, secrets, and auditability.
- Design enterprise IAM patterns (user context propagation, delegation chains, BFF sessions, least-privilege access) and OAuth/OIDC client models.
- Define standards for securing agent tools, data access, and cross-domain integrations; align to zero trust and regulatory controls.
- Produce architecture artifacts (CAD/HLD/PSS) and reference implementations.
- Lead and build IAM POCs (end-to-end flows, token exchange, gateway enforcement, delegated agent access).
- Configure/test identity flows; troubleshoot tokens, scopes, and integrations.
- Implement or guide IAM integrations across gateways, BFFs, agent orchestration, and observability.
- Evaluate and implement scalable Non-Human Identity (NHI) and secrets management solutions for agents and autonomous workloads, including credential issuance, rotation, and revocation.
- Design and implement agent identity at the gateway, including token validation, scope enforcement, and policy-driven access control.
- Automate agent onboarding workflows, including identity registration, entitlement assignment, and integration with IAM/PAM platforms.
- Define entitlement and data enforcement patterns to ensure agents access only authorized tools, APIs, and datasets.
- Ensure production-scale reliability, observability, and operational readiness for agent identity services.
- Design and test MCP-enabled identity integration flows for agent tools and external access.
- Enable secure external developer, agent, and MCP access through federation, scoped credentials, and gateway controls.
- Transition validated patterns to IAM engineering for production rollout.
- Define agent identity lifecycle (registration, credential rotation, revocation, environment separation).
- Integrate IAM across AI platform components; support CI/CD and IaC for IAM configurations.
- Establish patterns for human-in-the-loop controls, break-glass access, and rate limiting.
- Maintain documentation, decision records, diagrams, and runbooks.
- Deliver POC summaries, evaluations, and implementation guidance; communicate risks and dependencies.
- Ensure regulatory compliance; partner on threat modeling and controls (secrets, PAM, audit evidence).
- Serve as IAM SME for AI initiatives; mentor engineers.
- Deliver production-ready IAM patterns and reduce identity risk across AI workloads.
Requirements:
- 6+ years in IAM, security architecture, or platform engineering with significant IAM scope.
- 2+ years building IAM POCs and troubleshooting OAuth 2.0 / OIDC flows (Auth Code + PKCE, refresh tokens, client credentials, token exchange, OBO).
- 2+ years with PingOne AIC and/or Microsoft Entra ID.
Core Competencies:
- Hands-on experience designing identity for APIs, microservices, and BFF architectures.
- Experience integrating IAM with API gateways, AI/ML platforms, and modern application stacks.
- Strong knowledge of SAML, OAuth, OIDC, JWT, scopes, and authorization patterns.
- Familiarity with agent/tool identity models and secure integration patterns.
- Ability to translate AI requirements into secure identity designs; strong communication skills.
Preferences:
- Experience delivering AI/ML agents or copilots to production.
- Experience with SailPoint, CyberArk/Delinea, or Auth0/CIAM.
- Experience with one or more NHI or secrets-management solutions, such as Oasis Security, Astrix Security, Entro Security, Akeyless, CyberArk/Conjur, or HashiCorp Vault.
- Knowledge of AI-aware API gateways (e.g., Kong).
- Experience with IAM modernization or M&A programs.
- Relevant certifications (CISSP, CCSP, Entra, Ping, SailPoint, AWS).
- Familiarity with zero trust and identity threat detection.
#LI-Hybrid
Pay Range:
$122,673.00 - $204,455.00 ###
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 $122K-$204K range is below the median for AI Architect roles in our dataset (median: $183K across 30 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 8,943 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 $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($163K) sits 29% below the category median. Disclosed range: $122K to $204K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
LPL Financial AI Hiring
LPL Financial has 10 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Fort Mill, SC, US, Arlington, VA, Fort Mill, SC. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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