Principal AI/ML Engineer

Key Responsibilities:

AI Architecture & Technical Leadership 

• 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. 

Productize AI Research 

• 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. 

Engineering Excellence & Scalability 

• 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. 

Production Reliability & Operational 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. 

Mentorship & Thought Leadership 

• 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. 

Required Qualifications 

• 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 Qualifications 

• 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. 

What You'll Gain 

This role offers a unique opportunity to operate at the forefront of applied artificial intelligence and help bridge world-class research with real-world impact. 

You will: 

• Work directly with world-class AI researchers on breakthrough technologies and next-generation AI capabilities. 

• Own a critical position in the pipeline that transforms cutting-edge research into client value. 

• Tackle some of the most difficult AI engineering, scalability, and operational challenges in the industry. 

• Build AI capabilities that deliver meaningful business outcomes for clients. 

• Develop deep expertise in operating advanced AI systems at scale while collaborating with leaders across research, product, and engineering. 

Special Factors

Sponsorship

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.