AI Product Manager in City of London

Onsite based role in Abu Dhabi, UAE. Note: We provide employment visa and relocation assistance for those who are willing to consider this ONSITE Opportunity in Abu Dhabi, UAE.

Company Description

Open Innovation AI is a global technology company that specializes in developing advanced solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The platform is hardware-agnostic, optimized for various GPUs and accelerators hardware, and facilitates seamless integration and scalability for enterprise AI applications. Open Innovation AI focuses on optimizing and simplifying AI workload management and making AI technologies accessible to organizations of all sizes. With its innovative solutions, companies can reduce operational costs, accelerate time to value, and maximize their return on investment, ensuring that their AI strategies contribute directly to enhanced business outcomes.

Role Overview

We are looking for an AI Product Manager to lead the strategy, design, and delivery of AI-powered products and agentic systems that solve real customer problems at scale. You will work at the intersection of product, engineering, data, and business to transform foundation models, LLMs, and ML capabilities into reliable, secure, and commercially viable applications. This role requires strong product judgment, technical fluency with modern AI stacks (LLMs, RAG, agents, evaluation frameworks), and the ability to translate ambiguous AI possibilities into measurable business outcomes. You will own the full product lifecycle, from discovery and experimentation to production deployment, governance, and continuous optimization.

Key Responsibilities

• Product Strategy & Vision
  • Define the product vision, roadmap, and success metrics for AI/ML initiatives.
  • Identify high-impact use cases where AI provides defensible value (automation, augmentation, insights).
  • Translate business problems into AI capabilities and system designs.
  • Balance feasibility, cost (GPU/infra), performance, and ROI.
• Discovery & Problem Framing
  • Conduct user research to understand workflows, friction points, and opportunities for intelligence.
  • Validate ideas through rapid experimentation, pilots, and proofs-of-concept.
  • Frame problems as measurable hypotheses (latency, accuracy, cost per request, adoption, etc.).
• AI System Design & Delivery
  • Work closely with engineering to define RAG architectures, agentic workflows, model selection & benchmarking, prompt strategies, evaluation and guardrails.
  • Write clear PRDs and technical specs for AI features.
  • Prioritize features based on impact, risk, and technical complexity.
  • Lead sprint planning and cross-functional execution.
• Evaluation & Quality
  • Define AI-specific metrics: accuracy, hallucination rate, latency, cost/token, user satisfaction and task completion.
  • Implement evaluation pipelines and red-teaming processes.
  • Drive continuous improvements using experiments and feedback loops.
• Governance, Security & Risk
  • Ensure responsible AI practices: data privacy, bias mitigation, model governance, compliance requirements.
  • Partner with security/legal teams to implement guardrails and safe deployment.
• Stakeholder & Go-to-Market
  • Align leadership, engineering, and GTM teams on roadmap and outcomes.
  • Articulate technical tradeoffs to non-technical stakeholders.
  • Support launches, positioning, and customer enablement.
  • Track adoption and business outcomes post-launch.

Required Skills & Experience

• Product Fundamentals
  • 4 – 7+ years in Product Management or Technical Product roles.
  • Proven experience shipping complex software or platform products.
  • Strong PRD writing and roadmap ownership.
• AI / Technical Knowledge
  • LLMs and foundation models.
  • RAG pipelines.
  • Agents and orchestration frameworks.
  • APIs and system architecture.
  • Familiarity with prompt engineering.
  • Model evaluation & benchmarking.
  • Cost optimization (tokens/GPU/infra).
  • Experiment design (A/B testing, offline evals).
  • Ability to read architecture diagrams and collaborate deeply with engineers.
• Execution
  • Strong prioritization and tradeoff decision making.
  • Data-driven mindset.
  • Comfortable with ambiguity and rapid iteration.
  • Excellent written and verbal communication.