Principal AI/ML Engineer

The Vanguard Group•Charlotte, PA

46d•Hybrid

About The Position

This role focuses on defining and leading the technical architecture for enterprise-scale AI and ML platforms. The Principal AI/ML Engineer will design scalable, resilient, and reusable AI systems, establish architectural standards, and drive technical decisions around model serving, inference optimization, agent architectures, orchestration frameworks, observability, and AI infrastructure. A key aspect of the role involves partnering closely with AI researchers to transform prototypes into production-grade solutions, operationalizing advanced AI capabilities like Large Language Models (LLMs), Trustworthy and Responsible AI, and Agentic AI Systems. The position also emphasizes engineering excellence, solving complex AI engineering and scalability challenges, driving MLOps and LLMOps best practices, and ensuring the robustness and maintainability of AI products. Furthermore, the role requires owning operational excellence, reliability, performance, and availability of AI products, leading incident response efforts, and establishing best practices for AI system monitoring and management. Mentorship of AI and ML engineers and representing the team as a thought leader are also crucial components of this position.

Requirements

10+ years of experience in software engineering, machine learning engineering, AI engineering, or related technical disciplines.
Deep expertise designing, deploying, and supporting large-scale AI and ML systems in production environments.
Demonstrated success leading complex technical initiatives from concept through deployment and ongoing operations.
Strong knowledge of software architecture, reliability engineering, observability, ML Ops, DevOps, and cloud technologies.
Proven ability to mentor engineers and lead teams through highly complex technical and operational challenges.

Nice To Haves

Experience with foundation models, Large Language Models, and agentic AI architectures.
Experience deploying agentic AI systems and multi-agent workflows.
Experience with Trustworthy AI, Responsible AI, AI governance, or model risk management frameworks.
Experience optimizing large-scale inference systems and AI infrastructure.
Experience working in highly regulated environments and mission-critical production systems.

Responsibilities

Define and lead the technical architecture for enterprise-scale AI and ML platforms.
Design scalable, resilient, and reusable AI systems capable of supporting mission-critical workloads.
Establish architectural standards, engineering patterns, and best practices for AI deployment and operations.
Drive technical decisions around model serving, inference optimization, agent architectures, orchestration frameworks, observability, and AI infrastructure.
Partner closely with AI researchers to transform cutting-edge prototypes into production-grade solutions.
Lead efforts to operationalize advanced AI capabilities across areas such as: Large Language Models (LLMs), Trustworthy and Responsible AI, Agentic AI Systems.
Establish repeatable pathways that accelerate innovation-to-production cycles.
Ensure production solutions maintain scientific rigor while meeting enterprise engineering standards.
Bridge the gap between research breakthroughs and sustainable business value.
Solve the organization's most complex AI engineering and scalability challenges.
Design systems that operate reliably at enterprise scale while balancing performance, latency, governance, security, and cost.
Drive adoption of MLOps, LLMOps, and AI platform engineering best practices.
Improve the robustness, maintainability, observability, and operational readiness of our AI products.
Identify and eliminate architectural bottlenecks that impact scale, reliability, or client experience.
Raise standards through coaching, architecture reviews, design guidance, and technical leadership.
Own the operational excellence, reliability, performance and availability of our products.
Lead technical response and resolution efforts for complex production incidents, performance degradation, model failures, and system outages.
Serve as the senior technical escalation point for the team's most challenging production challenges.
Establish best practices for AI system monitoring, observability, alerting, incident management, capacity planning, and service-level objectives (SLOs).
Mentor and lead junior engineers in troubleshooting, root cause analysis, operational decision-making, and incident response.
Drive post-incident reviews focused on learning, continuous improvement, and long-term corrective actions.
Develop operational processes that ensure AI solutions remain secure, scalable, performant, and reliable for business-critical use cases.
Partner with product, infrastructure, security, and support teams to proactively identify operational risks and continuously improve service reliability.
Mentor AI and ML engineers within the team.
Foster a culture of technical excellence and operational ownership where engineers are accountable not only for building systems, but also for running and supporting them successfully in production.
Represent our team as a thought leader in scalable AI deployment, operational excellence, and responsible AI practices.