Product Manager

Product Manager – GPU Cloud & InferenceAbout the Company

Our client is a rapidly scaling GPU cloud service provider and LLM inference platform, delivering high-performance, scalable infrastructure tailored to the demands of machine learning, AI, and HPC workloads. With strong partnerships across the GPU ecosystem, they are building the next generation of AI infrastructure and services for enterprises and developers worldwide.

The Role

We are seeking a Product Manager to own the roadmap and execution for our inference engine, model library, and AI services. This role sits at the intersection of cutting-edge AI infrastructure and developer experience, focusing on making it easier for customers to deploy, scale, and optimize AI workloads on GPU cloud platforms.

You will:

• Define and deliver the product strategy for inference services, including model serving, optimization, and orchestration.
• Drive the development of a model library and associated developer tooling, enabling seamless integration of state-of-the-art models.
• Collaborate with engineering, solutions, and go-to-market teams to ensure successful launches and adoption.
• Translate customer needs into product requirements, balancing technical depth with usability and scalability.
• Monitor market trends in AI infrastructure and inference to maintain a competitive edge.
• Own the full product lifecycle, from discovery through delivery and iteration.

Requirements

• 4+ years’ experience in a Product Manager role within AI infrastructure, GPU cloud, HPC, or closely related domains.
• Demonstrated success delivering developer-focused products such as APIs, SDKs, or inference platforms.
• Strong technical understanding of AI/ML infrastructure, including GPUs, inference engines, and model deployment workflows.
• Proven ability to prioritize trade-offs across performance, scalability, and customer experience.
• Excellent communication skills with the ability to align engineering, sales, and customer stakeholders.
• A passion for building at the intersection of AI services and infrastructure.

Nice to Have

• Hands-on experience with model serving frameworks (e.g., TensorRT, Triton, vLLM, ONNX Runtime).
• Knowledge of container orchestration (Kubernetes, Docker) and infrastructure-as-code.
• Exposure to LLMs and generative AI use cases in production environments.
• Previous experience in a scaling cloud-native or HPC company.