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Vanguards 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 Vanguards 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.