AIML Engineer

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

• Bachelors degree in Computer Science Engineering Data Science Mathematics or a related technical field; Masters 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

About Vanguard

At Vanguard we dont just have a missionwere 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.