Senior AI/ML Research Engineer (Toronto)

Lead with purpose and keep growing. See your future here. 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. 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. Solid 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 Chance 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. Apply on Kit Job: kitjob.ca/job/2prl36