AVP, DevOps Engineer

LPL Financial•Charlotte, TX

About The Position

LPL Financial is seeking an experienced AVP of DevOps Engineering to lead a team that builds and operates an AI-driven observability and reliability platform for enterprise-scale environments. The role oversees strategy for monitoring LPL environments using Dynatrace and similar OpenTelemetry-based technologies, enabling intelligent automation and AI-powered operations, and advancing reliability engineering across the enterprise. It combines engineering leadership, SRE, platform engineering, observability, and applied AI/ML, with an emphasis on team development, operational excellence, automation, and business alignment. The successful candidate will lead reliability transformation in large-scale, mission-critical environments and collaborate across business and technology organizations. The position focuses on analyzing logs, metrics, and traces; implementing dynamic alerting; and delivering self-healing capabilities that reduce MTTR, improve resilience, and prevent outages. The leader will also establish reliability standards and governance, oversee incident management and continuous improvement, and communicate reliability outcomes to senior leaders and stakeholders.

Requirements

10 or more years of experience in Site Reliability Engineering (SRE), DevOps, Platform Engineering, Infrastructure Engineering, or Production Operations.
3 or more years of engineering leadership experience managing SRE, DevOps, Platform Engineering, or Operations teams.
7 or more years of experience leading automation initiatives using programming and scripting languages such as Python, Go, Java, or similar technologies.
2 or more years of experience with AI/ML-driven operational use cases, including anomaly detection, predictive monitoring, intelligent alerting, and incident correlation.
7 or more years of experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, container platforms, and modern CI/CD practices.
Deep knowledge of SRE principles, including SLIs, SLOs, error budgets, availability, resiliency, and operational excellence.
Expertise in enterprise monitoring, Dynatrace, logging, metrics, tracing, AIOps, and service health management.
Strong leadership in major incidents, root cause analysis, problem management, and continuous service improvement.
Ability to drive intelligent automation, Infrastructure as Code (IaC), CI/CD, and self-healing platform capabilities.
Understanding of AI/ML applications for anomaly detection, predictive monitoring, alert correlation, and operational intelligence.
Strong knowledge of cloud platforms, Kubernetes, distributed systems, scalability, resilience, and reliability patterns.
Ability to influence and partner effectively with engineering, infrastructure, security, architecture, and business leaders.
Strong executive communication, decision-making, program leadership, operational governance, and results-driven execution skills.
Strong collaboration skills and ability to deliver exceptional client experiences in fast-paced, team-oriented environments.

Responsibilities

Lead, mentor, and develop a high-performing Site Reliability Engineering team focused on observability, reliability, automation, and operational excellence.
Define and execute the strategic roadmap for enterprise observability, AI-driven operations, and reliability engineering.
Establish and govern reliability standards, including SLOs, SLIs, error budgets, monitoring practices, and operational readiness requirements.
Drive adoption of enterprise observability solutions, including Dynatrace, logs, metrics, traces, Real User Monitoring (RUM), and executive reporting dashboards.
Sponsor and oversee AI/ML-powered predictive monitoring, anomaly detection, incident correlation, alert optimization, and root cause analysis.
Lead major incident management, post-incident reviews, and continuous improvement to reduce MTTR, incident recurrence, and operational toil.
Partner with application, infrastructure, cloud, security, and architecture teams to embed reliability, resilience, and operational best practices throughout the software lifecycle.
Champion secure, auditable, scalable self-healing and intelligent automation aligned with governance requirements.
Drive platform engineering and Infrastructure-as-Code adoption across cloud and on-premises environments.
Establish metrics, KPIs, and operational governance frameworks to measure service health, team effectiveness, and reliability outcomes, and communicate progress to senior leadership and stakeholders.