AI Product Manager

Company Overview

Open Innovation AI is a global tech firm dedicated to pioneering solutions for managing artificial intelligence workloads. Their primary product, the Open Innovation Cluster Manager (OICM), efficiently orchestrates complex AI tasks across varied hardware environments, supporting multiple GPUs and accelerators. The platform is designed to be hardware-agnostic, enabling straightforward scaling and integration for enterprise AI implementations. Their mission centers on optimizing AI workload management to minimize operational expenses, expedite value realization, and maximize returns on AI investments, thereby enhancing business performance for organizations of all sizes.

Role Summary

The position of AI Product Manager entails leading the strategic planning, design, and rollout of AI-driven products and agentic systems that address significant customer challenges at scale. This role functions at the nexus of product development, engineering, data, and business domains to convert foundational AI models, large language models (LLMs), and machine learning capabilities into dependable, secure, and commercially successful applications. The ideal candidate should possess refined product intuition, technical proficiency with modern AI technologies (LLMs, retrieval augmented generation, agents, evaluation frameworks), and be adept at translating abstract AI potentials into concrete business results. Responsibilities cover managing the entire product lifecycle— from initial discovery and hypothesis testing to deployment, governance, and ongoing enhancement.

Key Responsibilities

• Define clear product vision, roadmap, and key success indicators for AI and machine learning projects.
• Spot and prioritize AI use cases delivering defensible competitive advantages such as automation, augmentation, and insights.
• Convert business challenges into AI system solutions balancing feasibility, infrastructure cost, performance, and ROI.
• Conduct thorough user research to map workflows, identify pain points, and opportunities for intelligence augmentation.
• Quickly validate ideas through experimentation, pilot programs, and proofs-of-concept.
• Frame challenges with measurable hypotheses focusing on latency, accuracy, operational cost, and adoption metrics.
• Partner with engineering teams to craft system designs including retrieval augmented generation architectures, agent workflows, model selection, prompt strategy, and evaluation guardrails.
• Create comprehensive product requirement documents and technical specifications for AI features.
• Prioritize product features considering impact, risk, and technical complexity.
• Lead sprint planning and cross-team collaboration to ensure execution alignment.
• Establish performance evaluation metrics including accuracy, hallucination rate, latency, cost per token, user satisfaction, and task success.
• Implement evaluation pipelines and red-teaming processes to drive continuous product improvement.
• Ensure adherence to responsible AI principles including data privacy, bias mitigation, model governance, and compliance.
• Collaborate with security and legal teams to enforce safeguarding measures for product deployment.
• Align key stakeholders across leadership, engineering, and go-to-market teams on product roadmaps and results.
• Communicate technical considerations clearly to non-technical stakeholders.
• Support product launches, positioning, and facilitate customer enablement.
• Monitor product adoption and business impact after launch.

Qualifications and Experience

• Between four to seven or more years in product management or technical product roles with a track record of launching intricate software or platform solutions.
• Experienced in writing detailed product requirement documents and owning product roadmaps.
• Strong technical background in large language models and foundational AI models.
• Hands-on knowledge of retrieval augmented generation pipelines, agent orchestration frameworks, APIs, and system architectures.
• Familiar with prompt engineering, AI model evaluation and benchmarking practices.
• Experience optimizing costs related to tokens, GPUs, and infrastructure usage.
• Skillful in experimental design including A/B testing and offline evaluations.
• Adept at reading architecture diagrams and closely collaborating with engineering teams.
• Excellent prioritization, making tradeoff decisions, data-driven problem-solving, and thriving in ambiguous, fast-moving environments.
• Superior verbal and written communication skills.