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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and leading the architecture of enterprise-scale AI and ML platforms, focusing on operational excellence, scalability, and responsible AI practices. Proven ability to mentor teams and drive technical decisions that enhance system performance and reliability.
Highest-signal resume keywords
AI And ML System DesignMLOps Best PracticesTechnical LeadershipObservability And MonitoringLarge Language Models
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software EngineeringMachine Learning EngineeringAI EngineeringReliability EngineeringCloud TechnologiesModel ServingInference OptimizationAI InfrastructureArchitectural StandardsIncident Management
Soft Skills
MentoringCoachingProblem-SolvingCollaborationTechnical Guidance
Industry Keywords
Trustworthy AIResponsible AIAI GovernanceModel Risk ManagementProduction EnvironmentsOperational ReadinessScalable AI DeploymentMulti-Agent WorkflowsPerformance DegradationCapacity Planning
Tech Stack
Tools & technologiesCloud
About the role
Key responsibilities & impact- Define and lead technical architecture for enterprise-scale AI and ML platforms
- Design scalable, resilient, and reusable AI systems for 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 with AI researchers to transform prototypes into production-grade solutions
- Operationalize advanced AI capabilities including LLMs, Trustworthy and Responsible AI, and agentic AI systems
- Establish repeatable pathways to accelerate innovation-to-production cycles
- Solve complex AI engineering and scalability challenges
- Design systems balancing performance, latency, governance, security, and cost
- Drive adoption of MLOps, LLMOps, and AI platform engineering best practices
- Improve robustness, maintainability, observability, and operational readiness of AI products
- Identify and eliminate architectural bottlenecks affecting scale, reliability, or client experience
- Provide coaching, architecture reviews, design guidance, and technical leadership
- Own operational excellence, reliability, performance, and availability of products
- Lead response and resolution for production incidents, performance degradation, model failures, and system outages
- Serve as senior technical escalation point for challenging production issues
- Establish practices for monitoring, observability, alerting, incident management, capacity planning, and SLOs
- Mentor junior engineers in troubleshooting, root cause analysis, operational decision-making, and incident response
- Lead post-incident reviews and long-term corrective actions
- Develop operational processes that keep AI solutions secure, scalable, performant, and reliable
- Partner with product, infrastructure, security, and support teams to identify operational risks and improve service reliability
- Mentor AI and ML engineers and foster technical excellence and operational ownership
- Represent the team as a thought leader in scalable AI deployment, operational excellence, and responsible AI practices
Requirements
What you’ll need- 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
- Preferred: Experience with foundation models, Large Language Models, and agentic AI architectures
- Preferred: Experience deploying agentic AI systems and multi-agent workflows
- Preferred: Experience with Trustworthy AI, Responsible AI, AI governance, or model risk management frameworks
- Preferred: Experience optimizing large-scale inference systems and AI infrastructure
- Preferred: Experience working in highly regulated environments and mission-critical production systems
- Must be able to work without employer-sponsored visa; Vanguard is not offering visa sponsorship
Benefits
Comp & perks- Hybrid working model enabling flexibility, in-person learning, collaboration, and connection
- Opportunities to work directly with world-class AI researchers
- Opportunity to develop deep expertise in operating advanced AI systems at scale
- Collaboration with leaders across research, product, and engineering