Senior Product Manager – MLOps

Radiant

. Own the strategy, roadmap, and delivery of Radiant’s MLOps and ML platform services .

Posted 10/6/2026full-timeLondon • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps and ML platform services, with a strong focus on product strategy, lifecycle management, and technical product ownership. Proficient in translating customer needs into actionable product requirements while ensuring high performance, reliability, and developer productivity.

Highest-signal resume keywords

MLOps Product OwnershipML Engineering WorkflowsKubernetes OrchestrationGPU Compute ManagementAPI Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model Lifecycle ManagementDistributed TrainingInferenceModel ServingExperiment TrackingPipelinesInfrastructure-as-CodeAPIsSDKsNotebooks

Soft Skills

PrioritisationCommunicationStakeholder Management

Tools & Technologies

PyTorchRaySlurmMLflowLangchainHugging FaceVLLMTritonContainersSchedulers

Industry Keywords

AI InfrastructureCloud PlatformsDeveloper PlatformsObservabilityNetworkingStorageIAM

Tech Stack

Tools & technologies

CloudKubernetesPyTorchRay

About the role

Key responsibilities & impact

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 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 versus buy versus partner decisions across the MLOps and AI infrastructure ecosystem

Requirements

What you’ll need

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 GPU compute, distributed training, inference, model serving, Kubernetes, containers, schedulers, distributed workloads, experiment tracking, model registries, pipelines, lifecycle management, APIs, SDKs, CLIs, notebooks, storage, networking, IAM, observability, and infrastructure-as-code
Familiarity with PyTorch, Ray, Slurm, MLflow, Langchain, Hugging Face, vLLM, Triton, or similar
Ability to balance performance, cost, usability, flexibility, and reliability
Strong prioritisation, communication, and stakeholder management skills
Comfortable taking ambiguous problems from discovery through delivery

Benefits

Comp & perks

25 days of annual leave
A culture that emphasises results over hierarchy, process & ego
Open communication and regular feedback
Dedicated learning time for new skills, projects or interests
Private medical insurance via Bupa
Cycle to Work Scheme
Gympass subscription to a variety of gyms and wellbeing apps
Participation in the company shares program
Enhanced parental pay & leave