The posting
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.
