Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own major AI inference product areas from discovery through launch adoption measurement and iteration. You will interview technical customers, define requirements and metrics, shape APIs and operational tooling, evaluate performance and economics, establish benchmarks, prioritize the roadmap, and align delivery across product engineering commercial QA support finance and leadership.
Requirements
- 6+ years of experience in product management or technical product leadership
- 3+ years working on AI or ML infrastructure inference platforms GPU cloud cloud infrastructure developer platforms APIs or technically complex B2B products
- Direct experience shipping products used for production AI workloads
- Strong understanding of model inference from API request through model serving and GPU execution
- Working knowledge of latency throughput batching caching quantization concurrency utilization scaling routing monitoring and failure handling
- Experience working with ML engineers AI engineers platform engineers developers and CTOs
- Experience translating customer workloads and technical constraints into product requirements
- Proven ownership from product discovery and definition through release and adoption
- Strong understanding of product metrics infrastructure economics pricing inputs and cost-performance trade-offs
- Experience working closely with engineering commercial QA support finance and leadership
- Strong written communication and ability to produce clear product documents
- Ability to operate effectively in a fast-moving startup with incomplete information
- Advanced English level C1
- Experience with vLLM SGLang TensorRT-LLM Kubernetes GPU infrastructure OpenAI-compatible APIs model gateways or enterprise AI controls is advantageous
Responsibilities
- Own major AI inference product areas from discovery through launch adoption measurement and iteration
- Work directly with AI-native companies and enterprise teams to understand workloads deployment requirements security needs and performance expectations
- Translate customer and business needs into product requirements acceptance criteria success metrics and release decisions
- Define the platform strategy across model APIs managed deployments developer interfaces routing observability usage billing and infrastructure control
- Shape the API documentation onboarding account experience usage visibility error handling and operational tools
- Define and interpret performance reliability utilization cost and margin metrics
- Define benchmark methodology and compare the product with market alternatives
- Prioritize product scope and protect the team from unnecessary complexity
- Break approved requirements into releases and manage dependencies and risks
- Align product engineering commercial QA support finance and leadership on product outcomes
- Define and track activation adoption workload growth retention reliability customer value and business performance
- Monitor inference providers AI infrastructure platforms developer tools and model-serving technologies
Benefits
- Performance-based incentives
- 24 days annual leave plus public holidays
- Health insurance
- Modern office in Yas Creative Hub