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.