Senior Product Manager, AI Inference Platform

01. ABOUT THE COMPANY

Dizzaract is a product-driven company operating at the intersection of gaming, digital platforms, and AI. We build and scale multiple products, including FAR Labs and Gamed. Each explores a different space, but all share the same approach: move fast, stay curious, and build things people actually use.
We operate as a collaborative, non-hierarchical team where ideas are valued based on their impact, not their origin. AI is embedded across everything we build, from infrastructure to product decisions.

02. ABOUT THE ROLE

FAR Labs is building FAR AI, an AI inference platform for developers, AI-native companies, and enterprises running production workloads.

The product covers the full path from model access and deployment to inference performance, APIs, usage visibility, billing, monitoring, and infrastructure control. Our customers care about latency, batched throughput, reliability, cost, security, privacy, and the ability to understand and control their AI workloads.

We are looking for a Senior Product Manager who has already worked on an AI inference platform, AI infrastructure product, GPU cloud, ML platform, developer API, or a closely related technical B2B product.

You will work directly with the CEO, Head of Product, engineering leads, and commercial team. You will turn customer and business problems into clear product decisions, requirements, metrics, and releases that Engineering can execute.

This is a senior individual contributor role. You will not simply collect requirements or manage tickets. You will own major product areas end to end, challenge assumptions, work directly with customers, and remain accountable for whether the product solves a real problem.

Your previous title may have been Senior, Staff, or Principal Product Manager, Technical Product Manager, Product Lead, Product Engineer, Head of Product. We care more about the product you owned than the title you held.

03. WHO YOU ARE

• Inference industry experience: You have worked on a product where customers deploy, serve, route, monitor, optimize, or pay for production AI inference workloads.
• Technically credible: You can work fluently with engineers on model serving, GPU infrastructure, APIs, batching, caching, quantization, routing, capacity, observability, and reliability.
• Customer-driven: You can speak with AI engineers, platform teams, CTOs, and enterprise buyers, understand their workloads, and separate real demand from feature requests.
• Strong product operator: You can take an unclear problem and turn it into scope, requirements, metrics, acceptance criteria, dependencies, and a practical release plan.
• Commercially aware: You understand that infrastructure performance only matters when it creates customer value and sustainable unit economics.
• Evidence-driven: You use customer interviews, workload data, benchmarks, competitive research, and product metrics to make decisions.
• Clear communicator: You can explain complex technical products in language that Engineering, Business, Support, and customers can all understand.
• Builder: You are comfortable establishing product practices from scratch and improving them as the company grows.

04. RESPONSIBILITIES

• Product ownership: Own major parts of FAR AI from discovery through launch, adoption, measurement, and iteration.
• Customer discovery: Work directly with AI-native companies and enterprise teams to understand their models, workloads, deployment requirements, security needs, performance expectations, and reasons for switching providers.
• Product requirements: Turn customer and business needs into clear PRDs, product boundaries, acceptance criteria, success metrics, and release decisions.
• Inference platform strategy: Help define how FAR AI develops across model APIs, managed deployments, developer interfaces, routing, observability, usage, billing, and infrastructure control.
• Developer experience: Shape the API, documentation, onboarding, account experience, usage visibility, error handling, and operational tools customers need to reach production.
• Performance and economics: Partner with Engineering and Product Analytics to define and interpret metrics such as TTFT, inter-token latency, batched throughput, goodput, utilization, reliability, cost per workload, breakeven point, and gross margin.
• Benchmarking: Define fair benchmark methodology and compare FAR AI with market alternatives under equivalent conditions. Turn technical results into clear product and commercial decisions.
• Prioritization: Decide what must ship now, what requires customer validation, and what should remain out of scope. Protect the team from unnecessary complexity.
• Roadmap and delivery: Work with Engineering to break approved requirements into releases, expose dependencies and risks, and keep product outcomes connected to execution.
• Cross-functional alignment: Keep Product, Engineering, Commercial, QA, Support, and leadership aligned on what is being built, why it matters, and what evidence will confirm success.
• Product metrics: Define and track activation, adoption, workload growth, retention, reliability, customer value, and business performance.
• Market intelligence: Follow inference providers, AI infrastructure platforms, developer tools, model-serving technologies, and enterprise AI requirements. Turn relevant changes into product decisions.

05. REQUIREMENTS

• 6+ years of experience in product management or technical product leadership.
• 3+ years working on AI/ML infrastructure, inference platforms, GPU cloud, cloud infrastructure, developer platforms, APIs, or another technically complex B2B product.
• Direct experience shipping a product used for production AI workloads.
• Strong understanding of how model inference works from an 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 technical customers such as ML engineers, AI engineers, platform engineers, developers, and CTOs.
• Experience translating customer workloads and technical constraints into clear product requirements.
• Proven ownership of products from 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 the ability to produce clear, decision-ready product documents.
• Ability to operate effectively in a fast-moving startup environment with incomplete information.
• English level: Advanced (C1).

Experience with vLLM, SGLang, TensorRT-LLM, Kubernetes, GPU infrastructure, OpenAI-compatible APIs, model gateways, or enterprise AI controls is a strong advantage.
Experience limited to consumer AI applications, prompt interfaces, or chatbot features without infrastructure or developer-platform ownership is unlikely to be sufficient for this role.

06. WHAT YOU WILL LEARN

• Inference economics: How model architecture, GPU selection, runtime configuration, traffic shape, utilization, and pricing combine into a viable inference product.
• AI infrastructure: How production inference systems manage models, requests, capacity, failures, monitoring, and distributed infrastructure.
• Technical product leadership: How to align infrastructure engineering, customer needs, product experience, and commercial constraints around one product direction.
• Enterprise AI: How security, privacy, governance, workload visibility, procurement, and operational evidence shape enterprise adoption.
• Category building: How to build and position a product in a fast-moving market where customer expectations and technical possibilities change quickly.

07. WHAT WE OFFER

• Real ownership and direct impact on the direction and execution of FAR AI.
• Direct collaboration with the CEO, Head of Product, engineering leads, and senior leadership.
• The opportunity to shape product strategy, requirements, metrics, customer experience, and ways of working.
• Hands-on exposure to technically challenging AI inference, infrastructure, and distributed systems problems.
• Direct contact with technical customers and enterprise decision-makers.
• Fast execution, low bureaucracy, and a highly collaborative, idea-driven team.
• Competitive salary with performance-based incentives.
• 24 days annual leave, plus public holidays.
• Health insurance.
• Modern office in Yas Creative Hub.
• Continuous learning through real-world problem solving.
• A diverse, open-minded team where ideas are genuinely heard.