AI/ML Engineer

Role Overview:

We are looking for a Machine Learning Engineer to join our team and play a critical role in developing scalable, production-ready AI/ML solutions. You’ll work closely with research scientists, software engineers, and business stakeholders to bring machine learning models from research to production, enabling impactful and innovative AI applications in areas such as generative AI, Agentic AI, and responsible AI

This is an exciting opportunity to work on state-of-the-art projects, including large scale language models, generative AI, cognitive systems, and real-time AI pipelines, all while ensuring the highest standards of performance and reliability.

Core Responsibilities:

• Design and implement robust pipelines to deploy machine learning models into production environments.
• Build and optimize scalable infrastructure for machine learning workflows, including data preprocessing, model training and inference.
• Optimize models for latency, accuracy, and efficiency in real-world scenarios.
• Work closely with data scientists to transition research models into production grade solutions.
• Develop and maintain automated workflows for version control, model monitoring, and retraining.
• Collaborate with data engineering teams to ensure efficient data pipelines and availability of high-quality datasets.
• Build tools to streamline machine learning development and deployment processes.
• Ensure deployed solutions adhere to responsible AI principles, focusing on safety.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science or AI related fields
• Proficiency in Python and frameworks such as Tensorflow, PyTorch, or Scikit-learn.
• Experience deploying ML models in cloud environments like AWS, GCP or Azure

• Familiarity with containerization tools (e.g., Docker) and orchestration frameworks (e.g., Kubernetes)
• Expertise in MLOps tools and practices, including CI/CD pipelines, model versioning, and monitoring
• Strong understanding of distributed systems and scaling machine learning workflows
• Solid knowledge of machine learning algorithms, model training, and evaluation techniques
• Experience working with NLP, computer vision, or generative AI models is a plus
• Demonstrated expertise in observability and monitoring using Grafana, Prometheus, and Splunk to ensure the reliability and performance of AI/ML solutions.
• Ability to work in cross-functional teams and communicate effectively with data scientists, engineers, and business stakeholders.
• Strong debugging and problem-solving skills to address technical challenges in deployment environments.

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.