AI/ML Engineer, Specialist

Core Responsibilities

1. Develops complex data pipelines and implements data engineering design principles for iterative data pipeline development to drive scale and efficiency. Proficient in model development environments and coding best practices to enable model deployment.
2. Integrates and optimizes existing data and model pipelines in a production environment. Identifies and diagnoses data inconsistencies and errors, documents assumptions, and forages to fill data gaps. Applies knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning concepts to create self-running artificial intelligence (AI) systems to automate predictive models. Proficient in SDLC processes and related tools and technologies.
3. Partners with data science teams to review model ready dataset document/feature documentation. Develops data model design and document and reviews for completeness with data science teams.
4. Partners with data science teams to understand data requirements, performs data discovery for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs. Drives efficiency through the use of data discovery tools.
5. Engages with internal stakeholders to understand and probe business processes and develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.
6. Writes model monitoring scripts as needed. Diagnoses root causes based on model monitoring alerts and triages issues. Coordinates and plans response to model monitoring alerts and resolves issues.
7. Serves as a machine learning engineering subject matter expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community.
8. Participates in special projects and performs other duties as assigned.

Qualifications

• Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
• Minimum of eight years related work experience.

Additional Details

1. Design and develop advanced AI/ML solutions - Builds production-grade AI systems including GenAI applications, RAG pipelines, and advanced ML models, ensuring scalability, reliability, and alignment with enterprise architecture standards.
2. Develop AI agents and multi-agent workflows - Designs and implements autonomous AI agents and multi-agent orchestration using frameworks such as LangGraph, enabling agentic automation of complex security and identity workflows with human oversight at critical decision points.
3. Lead LLM integration and prompt engineering - Develops and optimizes prompt engineering strategies, retrieval-augmented generation architectures, and model evaluation frameworks to deliver accurate, consistent, and defensible AI outputs for security and identity use cases.
4. Design and implement ML pipelines and data workflows - Builds end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, validation, and deployment, integrating with enterprise data sources such as ServiceNow, SailPoint, and security tooling.
5. Advance AI engineering standards and reusable patterns - Contributes to coding standards, reusable components, evaluation harnesses, and reference architectures adopted across AI initiatives, accelerating delivery and ensuring consistency across the portfolio.
6. Drive responsible AI and model governance compliance - Implements model risk management requirements including documentation, explainability, bias evaluation, and audit-ready evidence to ensure AI solutions meet regulatory and enterprise governance standards.
7. Architect and develop enterprise AI/ML solutions - Designs and builds production-grade AI systems including GenAI applications, RAG pipelines, and multi-agent workflows using LLMs, ensuring scalability, reliability, and alignment with enterprise architecture standards.
8. Lead LLM integration and prompt engineering - Develops and optimizes prompt engineering strategies, retrieval-augmented generation architectures, and model evaluation frameworks to deliver accurate, consistent, and defensible AI outputs for security and identity use cases.
9. Design and implement ML pipelines and data workflows - Builds end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, validation, and deployment, integrating with enterprise data sources such as ServiceNow, SailPoint, and security tooling.
10. Establish AI engineering standards and reusable patterns - Defines coding standards, reusable components, evaluation harnesses, and reference architectures adopted across AI initiatives, accelerating delivery and ensuring consistency across the portfolio.
11. Drive responsible AI and model governance compliance - Implements model risk management requirements including documentation, explainability, bias evaluation, and audit-ready evidence to ensure AI solutions meet regulatory and enterprise governance standards.
12. Mentor engineers and lead technical reviews - Provides technical guidance to AI/ML engineers across teams, conducts architecture and code reviews, and evaluates emerging AI technologies and frameworks for enterprise adoption.

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.