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.
Skills Required
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field
- 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 practices
- Experience with 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 across business and technology teams
- Master's degree
- Experience with Kafka, Flink, or Kinesis
- Experience with AI governance, Responsible AI, and model risk management frameworks
- Experience with enterprise-scale AI platform development and solution architecture
Vanguard Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Vanguard and has not been reviewed or approved by Vanguard.
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Retirement Support — Retirement support appears unusually strong through a 401(k) design that includes a match plus an additional employer contribution, which can materially lift long-term total rewards. HSA seeding and an enhanced employer match further strengthen the savings-and-benefits value of the package.
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Wellbeing & Lifestyle Benefits — Wellbeing and lifestyle support is reinforced by a sizable annual FlexFund stipend that can be applied across many day-to-day categories such as fitness, childcare, and other personal expenses. On-site or virtual clinics and fitness options add practical health and wellness convenience.
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Affordable Benefits — Healthcare and related benefits are positioned as comparatively affordable via heavily subsidized medical plans and broad coverage options. This affordability can offset moderate base pay for employees who place higher value on out-of-pocket cost reductions.
Vanguard Insights
What We Do
We are a community of 30 million who think – and feel – differently about investing. Together, we’re changing the way the world invests. Since our founding in 1975, helping our investors achieve their goals is our sole reason for existence. With no other parties to answer to and therefore no conflicting loyalties, we make every decision—like keeping investing costs as low as possible—with only your needs in mind. Vanguard is one of the world's largest investment companies, offering a large selection of high-quality low-cost mutual funds, ETFs, advice, and related services. Individual and institutional investors, financial professionals, and plan sponsors can benefit from the size, stability, and experience Vanguard offers. As of April 30, 2019, we managed more than $5.6 trillion in global assets. In addition, we have 189 funds in the United States and 225 funds in global markets. For Commenting Guidelines & Important information, visit here: http://vanguard.com/linkedin Vanguard Marketing Corporation, Distributor.
