A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
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. 0 Strong 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.
