AVP DevX - AI Engineer/Technical Lead

LPL Financial•Fort Mill, SC

1d•$147,500 - $245,900

About The Position

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.

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.
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.

Nice To Haves

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.

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.