VP - Principal Reliability Strategist

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What if you could lead the transformation of operational reliability and automation for a next-generation enterprise? At LPL Financial, we empower professionals to shape their success while helping clients pursue their financial goals with confidence. What if you could architect intelligent automation frameworks, pioneer AI-driven observability, and future-proof reliability strategies? If you're ready to take the next step, discover what’s possible with LPL Financial.

Job Overview:

LPL is seeking a visionary VP – Principal Reliability Strategist to drive enterprise priorities by enhancing availability, resiliency, and stability of cloud-native applications. This role will lead initiatives that reduce operational toil, enable predictive insights, and accelerate incident response through automation and AI-driven observability. You will blend engineering excellence with strategic foresight to strengthen our competitive edge.

Responsibilities:

Enterprise Reliability Leadership

• Champion availability, resiliency, and stability across distributed, cloud-native systems.

• Design and implement reliability strategies aligned with enterprise transformation goals.

Intelligent Automation

• Develop zero-touch operational workflows to eliminate manual interventions.

• Accelerate incident detection and resolution through automated remediation pipelines.

AI-Driven Observability

• Integrate AI/ML models for anomaly detection, predictive alerting, and root cause analysis.

• Collaborate with data science teams to embed observability intelligence into core platforms.

Scalable Framework Development

• Build reusable automation and AI pipelines aligned with enterprise architecture standards.

• Ensure frameworks are modular, extensible, and support rapid innovation.

Operational Efficiency

• Drive continuous improvement in MTTR, MTTD, and system uptime.

• Optimize resource utilization across infrastructure and application layers.

Strategic Innovation

• Position the organization for self-healing systems and autonomous operations.

• Lead initiatives that showcase modern engineering culture and intelligent operations.

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:

• 10+ years of experience in Site Reliability Engineering, DevOps, or Platform Engineering.

• Proven experience managing enterprise-scale infrastructure, platforms, and applications.

• Strong background in automation frameworks (Terraform, Ansible, CI/CD tools).

• Expertise in observability platforms (Datadog, Prometheus, Grafana, ELK, Splunk) and AIOps tools.

• Proficiency in Python, Go, or similar languages for automation and ML integration.

• Familiarity with ML/AI concepts and their application in operational contexts.

• Excellent communication and leadership skills with a strategic mindset.

Core Competencies:

• Experience with Kubernetes, service meshes, and container orchestration.

• Knowledge of SLOs, SLIs, and error budgets in reliability engineering.

• Exposure to generative AI and LLMs for operational intelligence.

• Experience with Big Data and cloud platforms.

• Design and develop AI/ML solutions for intelligent automation across enterprise IT environments

• Build cloud-native observability platforms with event correlation and predictive incident management capabilities

• Expertise in AIOps, including:

  • Noise reduction

  • Intelligent alerting

  • Self-healing mechanisms

  • Autonomous system design

• Advanced experience in cloud-native architecture and scalable infrastructure

• Hands-on experience with Agent AI technologies and frameworks

#LI-Hybrid

 

Pay Range:

$155,288-$258,813/year

 

Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrat