VPII, Software Engineering Manager & AI Lead - M&A & Partner Integration

LPL Financial•San Diego, CA

4d•$211,356 - $352,260

About The Position

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. 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.

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.
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.

Nice To Haves

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.

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.