Senior/Principal Product Manager - MLOps

About the role

About our team: Radiant is redefining how AI infrastructure is built. We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale — powering GPU-native workloads, multi-tenant control planes, and high-performance AI systems for the most demanding environments. We are building purpose-built AI infrastructure from powered land, to compute, to software. As we scale our operations and deploy capital into the next generation of AI infrastructure, we are looking to expand our finance team with leaders who can combine technical strength with execution excellence and are driven to build. Radiant was established by Brookfield, a leading global alternative asset manager with over US$1 trillion of assets under management across real estate, infrastructure, renewable power and transition, private equity and credit. Brookfield's global relationships, investment expertise and access to long-term institutional capital provide Radiant with a differentiated platform from which to develop, finance and operate AI infrastructure assets. This combination of entrepreneurial execution and institutional sponsorship enables Radiant to pursue large-scale GPU and AI infrastructure opportunities globally. Job Summary: As a Senior/Principal Product Manager - MLOps, you will own the strategy, roadmap, and delivery of Radiant’s machine learning platform and suite of AI/ML cloud services. You’ll work across GPU compute, training, inference, orchestration, model lifecycle, developer tooling, observability, and platform integrations, partnering closely with engineering, infrastructure, SRE, security, commercial teams, and customers. This is a highly technical product role focused on making it easier for customers to build, train, deploy, and operate ML workloads on Radiant’s neocloud platform.   Key Responsibilities: Own the strategy, roadmap, and delivery of Radiant’s MLOps and ML platform services. Define products across the ML lifecycle, including training, fine-tuning, model management, deployment, inference, and monitoring. Translate customer needs into clear product requirements, APIs, workflows, and priorities. Partner with engineering on GPU orchestration, Kubernetes, scheduling, storage, networking, and platform services. Work with ML engineers, researchers, and platform teams to improve developer experience, automation, and self-service. Drive prioritisation, delivery, launch, adoption, and continuous iteration. Define success metrics across performance, utilisation, reliability, adoption, and developer productivity. Evaluate build vs. buy vs. partner decisions across the MLOps and AI infrastructure ecosystem. Qualifications: Experience owning technical products, cloud platforms, MLOps products, developer platforms, or AI infrastructure. Strong understanding of ML engineering and model lifecycle workflows. Strong technical fluency across: ML infrastructure: GPU compute, distributed training, inference, model serving Orchestration: Kubernetes, containers, schedulers, distributed workloads ML platforms: experiment tracking, model registries, pipelines, lifecycle management Developer experience: APIs, SDKs, CLIs, notebooks Infrastructure: storage, networking, IAM, observability, infrastructure-as-code Familiarity with tools such as PyTorch, Ray, Slurm, MLflow, Langchain, Hugging Face, vLLM, Triton, or similar. Able to balance performance, cost, usability, flexibility, and reliability. Strong prioritisation, communication, and stakeholder management skills. Comfortable taking ambiguous problems from discovery through delivery. What you bring: ML platform thinking: Understand how compute, orchestration, training, deployment, and inference fit together. Product thinking: Turn complex AI infrastructure into simple, usable products. Technical fluency: Work credibly across GPUs, ML frameworks, distributed systems, storage, networking, and APIs. Customer empathy: Understand the needs of ML e

Skills