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About This Role
*Lead with Purpose, Unlock Your Team’s Passion*
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At LPL, people leaders hold the key to the employee experience — shaping culture, driving performance, and guiding individuals to new heights. Because when that happens, we all win – clients, LPL, and most importantly our, employees.
If you're ready to lead with intention and discover what’s possible, LPL Financial invites you to apply today.
Job Overview:
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We are building a next\-generation AI Ecosystem—an internal “App Store for AI\-enabled products” that enables teams to rapidly discover, integrate, and scale trusted AI capabilities across the enterprise.
This role will lead the strategy and execution of a developer\-first platform that standardizes how AI is built, secured, consumed, and scaled—driving adoption through reusable components, curated catalogs, and seamless integration into engineering workflows.
You will operate at the intersection of platform engineering, developer experience, and AI governance, shaping an ecosystem that accelerates innovation while maintaining enterprise trust and compliance.
Responsibilities:
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AI Ecosystem Platform \& Catalogs
- Design and operationalize a centralized AI App Store experience for internal developers
- Build and maintain curated catalogs, including:
+ MCP Server Catalog (Model Context Protocol servers)
+ Reusable Agents Catalog
+ Prompt and Context Libraries
+ Evaluation Templates and benchmarking frameworks
- Ensure discoverability, standardization, and high\-quality onboarding across all assets
Secure AI Access \& Integration
- Lead development of a secure AI browser experience and SaaS integrations
- Define patterns for safe, compliant AI consumption across tools and workflows
- Establish enterprise guardrails for identity, data access, and usage governance
API \& Gateway Enablement
- Extend and operationalize Kong AI Gateway capabilities
- Build and manage custom Kong plugins for:
- Enable consistent, secure, and scalable AI access patterns
Developer Experience \& Adoption
- Deliver a best\-in\-class developer starter kit, documentation, and quickstart guides
- Champion internal developer experience through:
+ Clear integration patterns
+ Self\-service onboarding
+ High\-quality documentation and examples
- Drive adoption through usability, reliability, and ecosystem engagement
Ecosystem Strategy \& Collaboration
- Define and execute an ecosystem\-first strategy across teams and domains
- Partner with platform, security, and application teams to ensure alignment
- Identify reusable capabilities and scale them across the organization
Impact
- Establish a standardized, secure, and scalable AI consumption model across the enterprise
- Accelerate delivery of AI\-enabled products through reusable components and patterns
- Reduce duplication and risk while increasing velocity and innovation
- Position the organization as a leader in enterprise AI platform engineering
What are we looking for?
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We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast\-paced, team\-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.
Requirements:
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- Minimum of 10 years of experience in software engineering, platform engineering, developer platforms, API ecosystems, or enterprise technology platforms.
- Minimum of 5\+ years of leadership experience managing platform, developer experience (DX), API, or AI engineering teams.
- Minimum of 3\+ years of experience building and scaling AI/LLM\-enabled platforms, products, or developer tooling, including agents, MCP servers, and AI integration patterns.
- Minimum of 3\+ years of hands\-on experience with API management platforms such as Kong, Apigee, MuleSoft, AWS API Gateway, or Azure API Management.
- Experience building and driving adoption of enterprise\-scale internal platforms, developer marketplaces, service catalogs, app stores, or ecosystem products used across multiple engineering teams.
Core Competencies
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- Proven Developer Champion mindset—deep empathy for internal engineering teams
- Developer Champion: Deep empathy for internal engineering teams with a focus on usability, simplicity, and developer productivity.
- Customer\-Obsessed: Prioritizes speed, clarity, and a best\-in\-class developer experience.
- Ecosystem Thinker: Designs reusable, composable solutions that scale across teams and business units.
- Adoption\-Focused: Measures success through platform usage, integration velocity, and developer satisfaction.
- Collaborative Leader: Builds alignment across platform, security, product, and engineering organizations.
- Bias for Action: Delivers practical, high\-impact solutions in a rapidly evolving AI landscape.
- Strategic Platform Builder: Proven ability to create and scale enterprise platforms, standards, and reusable capabilities.
Preferences:
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- Experience with: Kong AI Gateway / API gateways, MCP servers and emerging AI integration patterns, and Enterprise\-grade platform and tooling development
- Experience in building internal platforms, marketplaces, or ecosystem products
- Understanding of AI/LLM integration patterns, prompt engineering, and agent frameworks
- Experience implementing AI governance, security controls, identity and access management, and enterprise AI standards.
- Strong understanding of prompt engineering, RAG, context management, tool calling, and AI evaluation frameworks.
- Experience partnering with Engineering, Security, Architecture, and Product leaders to define platform strategy and execution.
Pay Range:
$163,900\.00 \- $273,100\.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 $163K-$273K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LPL Financial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Disclosed range: $163K to $273K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
LPL Financial AI Hiring
LPL Financial has 7 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Austin, TX, US, San Diego, CA, US, Fort Mill, SC, US. Compensation range: $191K - $352K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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