Machine Learning Engineer, Specialist

In this role you will provide;

Infrastructure and Environment Management:

• Design, implement, and manage scalable and reliable infrastructure for machine learning models, GenAi applications, and other enterprise applications.
• Ensure seamless integration of machine learning models into production systems.

Continuous Integration/Continuous Deployment (CI/CD):

• Develop and maintain CI/CD pipelines for automating the deployment of machine learning models.
• Implement version control and release management practices for machine learning assets.

Model Deployment and Monitoring:

• Deploy machine learning models to production environments and monitor their performance.
• Implement monitoring solutions to detect and address issues related to model drift, data quality, and system health.

Collaboration with Data Scientists and Engineers:

• Collaborate with data scientists to understand model requirements and facilitate the transition of models from research to production.
• Work closely with software engineers to integrate machine learning solutions into existing applications.

Security and Compliance:

• Implement security best practices for machine learning systems.
• Ensure compliance with relevant regulations and industry standards.

Automation and Scripting:

• Develop automation scripts and tools to streamline the MLOps processes.
• Implement best practices for code and configuration management.

Troubleshooting and Incident Response:

• Provide support for troubleshooting issues related to machine learning models in production.
• Develop and implement incident response plans for machine learning systems.

Documentation:

• Maintain comprehensive documentation for MLOps processes, workflows, and configurations

The Machine Learning Engineer writes model monitoring scripts as needed, diagnoses root causes based on model monitoring alerts while triages any issues, and coordinates and plans response to model monitoring alerts and resolves issues.

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

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

We are Vanguard. Together, we're changing the way the world invests.

For us, investing doesn't just end in value. It starts with values. Because when you invest with courage, when you invest with clarity, and when you invest with care, you can get so much more in return. We invest with purpose - and that's how we've become a global market leader. Here, we grow by doing the right thing for the people we serve. And so can you.

We want to make success accessible to everyone. This is our opportunity. Let's make it count.

Inclusion Statement

Vanguard's continued commitment to diversity and inclusion is firmly rooted in our culture. Every decision we make to best serve our clients, crew (internally employees are referred to as crew), and communities is guided by one simple statement: "Do the right thing."

We believe that a critical aspect of doing the right thing requires building diverse, inclusive, and highly effective teams of individuals who are as unique as the clients they serve. We empower our crew to contribute their distinct strengths to achieving Vanguard's core purpose through our values.

When all crew members feel valued and included, our ability to collaborate and innovate is amplified, and we are united in delivering on Vanguard's core purpose.

Our core purpose: To take a stand for all investors, to treat them fairly, and to give them the best chance for investment success.

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.