Machine Learning Engineer - Enterprise AI & Research (EAiR)
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Vanguard’s Enterprise AI & Research (EAiR) team is building the next generation of AI capabilities that will power enterprise-scale products and experiences across Vanguard. Our team operates at the intersection of applied AI, platform engineering, and production-scale machine learning systems.
We are seeking a hands-on Machine Learning Engineer with strong software and cloud engineering skills to help design, deploy, and scale AI/ML applications and infrastructure in a highly collaborative enterprise environment. This role is ideal for engineers who enjoy solving complex technical problems across Kubernetes, cloud-native architectures, LLM applications, MLOps/LLMOps, and distributed AI systems.
You will partner closely with AI researchers, product leaders, platform teams, and application engineers to operationalize advanced AI capabilities into reliable, scalable production systems.
Responsibilities:
Design, build, deploy, and maintain scalable AI/ML systems and services in cloud-native environments
Develop and support production-grade machine learning and generative AI applications deployed on Kubernetes/EKS platforms
Build and optimize model inference pipelines, APIs, orchestration layers, and supporting infrastructure for AI workloads
Partner with AI researchers to transition prototypes and proof-of-concepts into secure, observable, and production-ready enterprise solutions
Improve platform reliability, scalability, monitoring, resiliency, and operational excellence for AI systems
Contribute to CI/CD pipelines, infrastructure-as-code, deployment automation, and engineering best practices for AI applications
Support GPU-enabled workloads, distributed compute environments, and high-performance inference/training systems
Collaborate with cross-functional teams including Product, AI Research, Security, Architecture, and Enterprise Platform Engineering
Participate in troubleshooting and root-cause analysis for complex distributed systems and AI platform issues
Help define engineering standards, operational processes, and best practices as Vanguard continues scaling enterprise AI capabilities
Qualifications:
5+ years of experience in software engineering, machine learning engineering, platform engineering, or related technical roles
Strong programming experience in Python and modern software engineering practices
Experience deploying and operating applications in Kubernetes environments (EKS preferred)
Hands-on experience with cloud platforms such as AWS
Experience building, deploying, or supporting ML/AI systems in production environments
Familiarity with containerization technologies such as Docker and orchestration frameworks such as Kubernetes
Experience with CI/CD pipelines, infrastructure automation, monitoring, and observability tooling
Understanding of distributed systems, scalable APIs, and microservice architectures
Experience working with LLMs, generative AI applications, vector databases, inference systems, or MLOps/LLMOps tooling is strongly preferred
Strong collaboration and communication skills with the ability to work across research and engineering organizations
Preferred Qualifications:
Experience supporting GPU-based workloads and AI infrastructure
Experience with ML model deployment frameworks and inference optimization
Familiarity with observability and monitoring tools such as Grafana, Splunk, CloudWatch, Prometheus, or similar technologies
Experience building enterprise AI systems with reliability, governance, and security considerations
Exposure to Responsible AI concepts and enterprise AI governance practices
What Sets This Role Apart:
Opportunity to help shape Vanguard’s enterprise AI ecosystem
Work on real-world AI systems deployed at enterprise scale
Exposure to cutting-edge AI technologies including generative AI and agentic systems
High-impact engineering role with significant ownership and influence
Collaborative environment bridging research, engineering, and product delivery
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.