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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.
Are you a hands\-on engineer who enjoys designing platforms, shaping developer workflows, and reducing friction across the software delivery lifecycle? Are you excited by embedding AI and GenAI directly into how developers build, test, and ship software? If so, LPL Financial offers an opportunity to work at the intersection of platform engineering, developer experience, and applied AI at enterprise scale.
We remain committed to the advisor\-centered model and to expanding access to personalized financial guidance. At LPL, independence means advisors choose the business model, services, and technology that enable them to run their ideal practice — and we focus on supporting advisors so they can focus on their clients.
Job Overview
As AVP, Software Engineer, you will serve as a senior individual contributor and technical leader within the Developer Experience Platform and PDLC AI Enablement domain. This role focuses on two closely connected areas:
Internal Developer Platform (IDP) — delivering self\-service automation, golden paths, and reference architectures. AI and GenAI enablement — providing production\-ready RAG pipelines, agents, plugins, and AI\-assisted development tools as first\-class platform capabilities.
This role is intentionally hands\-on. You will write production code, review pull requests, pair with engineers on AI prototypes, and shape platform and AI architecture. While you will influence teams, roadmaps, and executive stakeholders, this role does not include direct people management.
Responsibilities
- Design, build, and evolve cloud\-native platform services and AI/GenAI capabilities that improve developer productivity and software delivery outcomes.
- Architect and deliver enterprise\-grade AI solutions, including RAG and GraphRAG pipelines, multi\-agent systems, LLM\-powered developer tools, plugins, MCP tools, and reusable skills.
- Drive Claude Suite adoption end\-to\-end, including claude.ai, Claude API, Claude Code, system prompt design, and context window management.
- Lead large\-scale Cursor AI integration, including workspace rules, .cursorrules governance, MCP server connections, and AI\-assisted development workflows.
- Build and operate LLM evaluation pipelines using RAGAS, LangSmith, and custom evaluation harnesses; implement AI observability such as tracing, hallucination detection, latency and token cost dashboards, drift monitoring, and guardrails.
- Contribute to the build\-out of the Internal Developer Platform, including Backstage plugin development, golden\-path templates, and self\-service portals for application, infrastructure, and architecture automation.
- Participate in Agile ceremonies including Scrum events and PI Planning; collaborate with Product, Architecture, Security, and Platform partners on delivery planning and technical design.
- Facilitate solution design sessions, technical demos, Lunch \& Learn events, office hours, and developer community forums to share knowledge and address engineering pain points.
- Translate developer feedback and partner team needs into scalable platform and AI capabilities.
- Author and maintain technical documentation including design documents, runbooks, disaster recovery strategies, and release documentation in alignment with enterprise standards.
- Develop and maintain web, API, and AI\-enabled services, including secure coding practices, vulnerability remediation, testing, and CI/CD automation.
- Partner closely with Architecture, Security, Cloud, DevSecOps, Quality Engineering, Compliance, and Release teams to define and promote reference architectures and best practices.
- Embed DevSecOps and Responsible AI principles, including shift\-left security, SAST/DAST integration, SBOM generation, software supply chain controls, and AI governance.
- Track and improve platform adoption metrics, AI usage and quality indicators, and developer experience outcomes.
- Present platform architecture, AI capabilities, and technical recommendations to senior leadership and executive stakeholders.
- Continuously identify opportunities for automation and innovation that simplify workflows and enhance the developer experience across the enterprise.
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
- 10\+ years of hands\-on, end\-to\-end application development experience across .NET, Angular, React, TypeScript, and Python in cloud\-hosted (AWS), microservice\-based environments.
- 2–5\+ years of hands\-on experience delivering AI/ML and GenAI solutions in enterprise production environments.
- 2\+ years experience with the Claude Suite, including claude.ai, Claude API, Claude Code, system prompt design, and context window management.
- 2\+ years Architect\-level experience with RAG and GraphRAG pipelines, including chunking strategies, embedding models, and vector stores such as Pinecone, pgvector, or Azure AI Search.
- 2\+ years experience designing and implementing AI agents, including multi\-agent orchestration, tool use, memory strategies, and agent communication protocols.
Core Competencies
- Designing complete cloud solutions with consideration for scalability, security, data protection, disaster recovery, FinOps (including AI workload cost), and compliance.
- API design expertise, including OpenAPI, GraphQL, versioning strategies, rate limiting, and gateway integration.
- Experience with GitOps, blue\-green and dark release deployment strategies, and Infrastructure as Code.
- Track record of contributing to and scaling Internal Developer Platforms, including paved paths, golden paths, and self\-service portals.
- DevSecOps and InnerSource practices, including contribution models, discoverability, reuse, and security automation.
- Commitment to automated testing, code quality, and continuous improvement using industry\-standard tools and frameworks.
- Clear written and verbal communication, with the ability to explain complex platform and AI concepts to technical and non\-technical audiences.
- Ability to influence cross\-functional teams and senior stakeholders through technical expertise and collaboration rather than formal authority.
Preferences
- Master’s degree in Computer Science or a related field.
- AWS certifications (Developer, Solutions Architect, or equivalent).
- Experience with graph databases such as Neo4j or Amazon Neptune.
- Experience with OpenTelemetry custom instrumentation.
- Familiarity with advanced prompt engineering patterns (e.g., React, SELF‑RAG) and model fine\-tuning approaches
- Experience working within Responsible AI and AI governance frameworks.
- Demonstrated curiosity, adaptability, ownership mindset, and comfort working in fast\-changing technical environments.
- Experience with LLM orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGen.
Pay Range:
$147,500\.00 \- $245,900\.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 $147K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($196K) sits 10% above the category median. Disclosed range: $147K to $245K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
LPL Financial AI Hiring
LPL Financial has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety. Positions span Austin, TX, US, Fort Mill, SC, US, New York, NY, US. Compensation range: $182K - $292K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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