LPL Financial is seeking a hands-on AI engineering leader to own the Tenant Engine, a critical AI-powered static-analysis and remediation framework supporting a high-visibility, portfolio-scale multi-tenant migration. This role is ideal for a builder who can combine technical depth in LLM/GenAI systems, disciplined engineering execution, and people leadership to improve code remediation quality, scan throughput, and operating cost at scale. 

Job Overview 

The VPII, Software Engineering Manager & AI Lead - M&A & Partner Integration owns the day-to-day operations, roadmap, and delivery performance of LPL’s Tenant Engine. Reporting to the SVP, Technology, this leader directs regeneration cycles across scanning, noise filtering, LLM validation, and remediated-code generation; approves noise-filter rules and validator/sampler prompt updates; oversees scan operations across a large repository portfolio; and coordinates engine-to-migration handoffs with DB & App and E2E Quality Engineering leads. The role is accountable for the quality, throughput, actionability, and cost of the engine’s output. 

Responsibilities 

• Own the Tenant Engine roadmap and operating rhythm: prioritize remediation automation, portfolio-scale scanning, and the SME-gated regeneration loop to keep migration work moving on cadence. 

• Direct regeneration cycles: lead each scan → noise-filter → LLM-validation → remediated-code generation cycle, incorporating SME feedback into successive rounds and sustaining measurable progress through the Discovery loop. 

• Govern noise-filter rules and prompts: review and approve NF rule changes and validator/sampler prompt iterations, balancing false-positive reduction with recall, must-fix coverage, and migration risk. 

• Lead and develop the team: supervise the Applied AI Engineer, AI Platform Engineer, and scan-operations analysts; set goals, remove blockers, and run the weekly engine standup. 

• Oversee scan operations at scale: hold accountability for scan coverage, SLA performance, output integrity, and per-finding cost across approximately 900+ repositories in partnership with platform engineering. 

• Coordinate cross-track handoff: partner with DB & App and E2E QE leads to move remediated findings into migration execution and represent the engine in Architecture Review Board decisions related to G-category RLS/batch work. 

• Report quality and economics: translate false-positive rate, finding actionability, throughput, and unit-cost trends into clear updates for senior leadership and governance forums. 

• Build operational independence: establish runbooks, processes, and team capability so the engine can operate, improve, and scale without executive intervention. 

What are we looking for? 

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 

• AI/ML and engineering leadership: 10 or more years of progressive software, AI, ML, platform, or data-intensive engineering experience, including 5 or more years in AI/ML or platform technical leadership and 3 or more years directly leading engineering teams. 

• Production LLM/GenAI ownership: Experience owning production LLM/GenAI systems, including prompt and evaluation pipelines, LLM validation at scale, and output quality/cost gating on AWS Bedrock or an equivalent foundation-model platform. 

• Roadmap and delivery at scale: Experience owning a technical roadmap and deliver across teams in a large-scale or regulated program, including systems operating at portfolio scale and delivery against hard deadlines. 

• Static analysis and automated remediation: Experience leading large-scale code analysis, automated remediation, developer tooling, or similar engineering productivity programs with the depth to review code, prompts, and architecture decisions. 

• Education: Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience; Master’s degree preferred. 

Core Competencies 

• Hands-on technical leadership: Earns credibility through sound technical judgment while developing the team to operate with increasing independence. 

• Systems thinking and prioritization: Optimizes the full scanning, validation, remediation, and handoff pipeline while focusing scarce SME and engineering capacity on must-fix work. 

• Decisive executive communication: Makes evidence-based decisions on NF rules and prompt changes, then communicates quality, cost, throughput, and risk clearly to senior stakeholders. 

Preferences 

• Experience operationalizing AI in a regulated financial-services or other compliance-driven environment. 

• Familiarity with AWS-native ML/data infrastructure such as Bedrock, EKS, Neptune, S3, Step Functions, and infrastructure-as-code practices. 

• Background in multi-tenancy, platform consolidation, or large-scale application-modernization programs.