Senior AI/ML Scientist

Responsibilities

Solve Business Problems with AI

• Design and build advanced ML models that integrate multi-dimensional data into insights and signals that drive critical business decisions.
• Design and build enterprise knowledge systems that integrate structured and unstructured data across multiple business platforms, enabling AI agents to retrieve, reason over, and operationalize trusted organizational knowledge.
• Partner with business stakeholders to identify, frame, and prioritize high‑value problems that can be addressed using Agentic AI, LLMs, and ML.
• Define and implement business-centric evaluation frameworks that measure coverage, relevance, trustworthiness, explainability, and user adoption in addition to technical model performance.
• Focus on business outcomes, not just model performance.

Design & Build Agentic AI Solutions

• Architect and develop agentic AI systems that can reason, plan, and take actions across tools, workflows, and data sources.
• Design multi‑agent and tool‑augmented LLM solutions to automate complex, multi‑step processes.
• Ensure solutions are reliable, explainable, and governed for enterprise use.

Scalable & Responsible AI

• Collaborate with engineering teams to deploy AI solutions with scalability, security, and performance in mind.
• Implement evaluation, monitoring, and guardrails for LLM and agentic systems, including bias, drift, and failure modes.
• Align solutions with enterprise risk management, compliance, and responsible AI standards.

Thought Leadership & Collaboration

• Act as a trusted AI advisor, helping teams understand where Agentic AI and LLMs add value—and where they do not.
• Contribute to AI best practices, reusable patterns, and strategic direction.
• Mentor peers and teammates on applied AI and business‑driven problem solving.

Qualifications

• Agentic AI: Experience designing AI agents that reason, plan, and act across systems.
• Large Language Models (LLMs): Hands‑on experience building enterprise LLM applications (e.g., RAG, tool use, orchestration, evaluation).
• Natural Language Processing (NLP): Strong experience working with unstructured text and language‑driven workflows.
• ML: Hands on experience with Gradient Boosting methods, familiar with preeminent hyper-parameter tuning and interpretability options.
• MS or PhD in Computer Science, Machine Learning, Data Science, or a related quantitative field.
• 3+ years delivering AI/ML solutions in production environments.
• 5+ years of hands‑on Python experience; experience with distributed data processing is a plus.
• 0Strong ability to solve business problems using AI, not just build models.
• Excellent communication skills, with the ability to explain complex concepts to both technical and non‑technical audiences.
• Experience working in cross‑functional, enterprise environments.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

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.