About this role
Responsibilities
• Design, develop, and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management. • Build scalable, cloud-native AI/ML applications and services using AWS technologies such as SageMaker, ECS, Lambda, S3, EventBridge, and Step Functions. • Develop and maintain machine learning, data engineering, and MLOps pipelines supporting batch and real-time workloads. • Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Advanced RAG, vector databases, knowledge retrieval systems, agentic AI frameworks, and fine-tuning techniques. • Design and utilize knowledge graphs, graph databases, and relationship-based analytics to enhance enterprise intelligence and decision-making. • Partner with business stakeholders to translate business challenges into scalable analytical and AI-driven solutions. • Conduct data discovery and exploratory analysis, establish data lineage, and perform root cause analysis to ensure data quality and reliability. • Implement model monitoring, observability, alerting, and operational support processes for production AI/ML solutions. • Ensure adherence to enterprise AI governance, security, Responsible AI, privacy, and model risk management standards. • Serve as a machine learning engineering subject matter expert, lead technical design discussions, and mentor team members on AI/ML best practices. • Stay current on emerging AI technologies and evaluate their application to business opportunities.
Qualifications
• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; Master's degree preferred. • 6+ years of experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a related discipline. • 3+ years of hands-on experience building scalable data pipelines and ETL solutions using AWS services. • Strong proficiency in Python and modern software engineering practices. • Experience deploying and supporting production-grade AI/ML applications in cloud environments, preferably AWS. • Strong experience with SageMaker, MLOps, CI/CD pipelines, model deployment, monitoring, and Machine Learning Development Lifecycle (MDLC) practices. • Experience with containerization and orchestration technologies such as Docker, ECS, and Kubernetes. • Experience with Generative AI technologies, including LLMs, Advanced RAG, vector databases, semantic search, agentic AI frameworks, and enterprise knowledge retrieval systems. • Experience designing and implementing knowledge graph solutions and graph databases. • Strong understanding of software engineering fundamentals, including system design, testing, security, observability, and version control. • Ability to lead technical initiatives, influence architectural decisions, and collaborate effectively across business and technology teams.
Preferred Experience
• Real-time data processing and streaming technologies such as Kafka, Flink, or Kinesis. • AI governance, Responsible AI, and model risk management frameworks. • Enterprise-scale AI platform development and solution architecture. 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.