VPII, Software Engineering Manager & AI Lead - M&A & Partner Integration
LPL FinancialAbout the role
Lead with Purpose, Unlock Your Team’s Passion
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.
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
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