Responsibilities

📍Product Strategy & Vision: Define and articulate the product vision, strategy, and a roadmap for AI and intelligent automation initiatives, aligning with Keyrock's overall business objectives and technology strategy. 📍AI Application & Automation: Drive the practical application of AI by identifying key use cases and working closely with engineering teams to integrate LLMs, MCP, and other state-of-the-art automation technologies into existing and new products, after prioritising. 📍Technology Integration: Oversee the end-to-end integration of AI models and tools into Keyrock's existing technological ecosystem, ensuring seamless operation, scalability, and maintainability, through use case definition, documentation, and driving QA. 📍Financial Context & Time Series Data: Leverage your understanding of financial markets and expertise in time series data manipulation to identify opportunities for AI to enhance trading strategies, risk prediction, and operational insights. 📍Human-AI Teaming & Productivity: Champion a vision for accelerating human-AI collaborative intelligence applications. This involves discovering & designing products that enhance teams’ decision-making, automate repetitive tasks, and create efficient and valuable workflows between human operators and intelligent systems. 📍Process & Capability Shaping: Actively shape organizational processes, standards, and capabilities related to AI development and deployment. This includes defining, promoting, and iterating best practices for data governance, model validation, and responsible AI usage, in conjunction with governance teams for relevant areas. 📍Stakeholder Collaboration: Work closely with cross-functional teams including Commercial, Trading, Research, Engineering, Operations, and Compliance to discover opportunities, gather requirements, define product specifications (with Engineering), and ensure successful product delivery that complies with the needs of regulatory and control functions. 📍Market & Technology Research: Stay abreast of the latest advancements in AI, machine learning, data engineering, and automation, identifying emerging technologies and trends that can be leveraged to create competitive advantages. 📍Performance & Impact Measurement: Define key metrics and KPIs to measure the success and impact of AI-powered products, iterating based on data and feedback. Be able to identify why one piece of work is prioritised over another through (simple) models of impact.

Essentials

📍Experienced Product Leader: 5+ years of product management experience, with a proven track record of successfully launching and scaling complex technology products. 📍AI/ML Expertise: Demonstrated experience with Artificial Intelligence and Machine Learning, with a particular emphasis on practical application of LLMs, MCP, and other advanced automation paradigms. Relevant portfolios welcome. 📍Technical Acumen & Data Engineering: Strong technical understanding of software development, data architectures, and API integrations. “Just enough” level of experience in data engineering, including building and maintaining data pipelines, is highly valued. Ability to engage in technical discussions with engineering teams. 📍Financial Domain Exposure: Prior exposure to financial contexts, market data, and time series data manipulation is highly desirable. Experience in fintech, trading, or quantitative finance is a significant plus.

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