Keyloop bridges the gap between dealers, manufacturers, technology suppliers and car buyers.
We empower car dealers and manufacturers to fully embrace digital transformation. How By creating innovative technology that makes selling cars better for our customers and buying and owning cars better for theirs.
We use cutting-edge technology to link our clients' systems, departments and sites. We provide an open technology platform that's shaping the industry for the future. We use data to help clients become more efficient, increase profitability and give more customers an amazing experience. Want to be part of it
Purpose of the Role
- The Product Owner reports into the Head of Data Products and leads the development of machine learning and agentic capabilities embedded throughout our platform. Working closely with Agile development teams, this role drives the delivery of LLM-powered agents, predictive models, and intelligent automation features that transform automotive retail. This position ensures strategic alignment with product vision while managing the intersection of data engineering and ML capabilities, to realise AI-powered solutions
Responsibilities -The Product Owner will be responsible for leading the development of AI capabilities that will be integrated throughout our Automotive Retail Platform called 'Fusion'. These will be embedded directly into our platform, SaaS solutions and VEGA, our AI-powered agentic business intelligence solution. This role will lead two teams responsible for AI engineering and our data infrastructure to power AI solutions. The person will :
Lead execution of the product development roadmap for machine learning capabilities and LLM-powered agents, defining features that deliver measurable business valueOwn and prioritise the engineering team backlog for AI / ML features, ensuring high-value deliveryExecute the integration strategy for embedding LLM agents and ML models throughout Fusion, working closely with domain product and engineering teams to ensure seamless implementationsLead cross-functional teams in engineering data pipelines, feature stores, and data infrastructure specifically designed to power AI and ML solutionsWrite, refine, and prioritise epics and user stories for ML model development, LLM agent capabilities, data engineering requirements, and AI-powered features with clear acceptance criteriaCollaborate with Data Scientists, ML Engineers, and AI specialists during Iteration Planning, stand-ups, and reviews to clarify requirements for model training, agent behaviour, and data preparationDefine success metrics for AI products including model performance, agent accuracy, response quality, user adoption, and business impact, continuously monitoring and optimising based on these KPIsAct as the voice of the customer for AI capabilities, ensuring LLM agents and ML features meet real-world needs of automotive retailers and deliver intuitive, trustworthy experiencesAccept completed user stories, ensuring they meet the Definition of Done for model accuracy, agent reliability, data quality, and production readinessCollaborate with the Head of Data Products to align AI / ML roadmap with Keyloop's overall Fusion product strategyTranslate AI product strategy into actionable concepts and detailed technical requirements for self-service ML tools, embedded intelligence, LLM orchestration, and real-time inference capabilitiesChampion responsible AI practices, ensuring fairness, transparency, explainability, and ethical considerations are built into all AI / ML productsDrive a data-first, AI-powered culture that transforms how automotive retailers understand and optimise their business through intelligent automation and predictive insightsLeverage deep AI product expertise to communicate effectively across stakeholders including Product Managers, Data Scientists, ML Engineers, Data Engineers, Analytics Engineers, and UX specialistsForge close relationships with product teams, engineering squads and data teams based globally, with most daily interactions being with colleagues based in the United Kingdom and Poland, which this role directly supportsSkills, know-how and experience- Must Have :
Proven experience owning and delivering AI / ML products from concept to production, including hands-on work with machine learning models and LLM-powered applicationsStrong understanding of machine learning product lifecycle, including model development, training, evaluation, deployment, monitoring, and continuous improvementGood understanding of data engineering for AI, including data pipelines, feature engineering, data quality for ML, and MLOps practicesProven ability to write clear and concise epics and user stories for complex AI features, data engineering tasks, and ML model requirementsStrong communication skills to translate complex AI / ML concepts to non-technical stakeholders and bridge between Data Scientists, ML Engineers, and global business teamsAbility to define and track AI product success metrics including model performance, business impact, user satisfaction, and ROI of AI investmentsExperience leading cross-functional AI teams including Data Scientists, ML Engineers, Data Engineers, and understanding their workflows and collaboration patternsFluent in English languagePreferred (not essential but advantageous) :
Experience in a Scaled Agile environment (e.g., Scrum@Scale, SAFe) specifically for AI / ML product developmentExperience with embedded AI in SaaS products, including considerations for latency, cost optimization, and user experience of AI featuresKnowledge of the automotive retail industry and data sources that can be leveraged for AI solutionsUnderstanding of modern AI / ML technology stack including LLM platforms, ML frameworks, vector databases, model serving infrastructure, and AI orchestration toolsFamiliarity with product tools such as Jira, Confluence, or similar, plus ML-specific tools like MLflow, Weights & Biases, or similarExperience with generative AI products, prompt engineering, RAG (Retrieval-Augmented Generation) systems, and LLM fine-tuningExperience with major LLM platforms (OpenAI, Anthropic, Google, Azure OpenAI) and agent frameworks (LangChain, LlamaIndex, AutoGPT)Knowledge of MLOps best practices, including CI / CD for ML, model versioning, A / B testing for models, and production monitoringUnderstanding of responsible AI principles, model explainability (XAI), bias detection and mitigation, and AI governance frameworksExperience with cloud-based AI / ML platforms (AWS SageMaker, Azure ML / Databricks, Google Vertex AI)Masters or undergraduate degree in Computer Science, Information Technology, Data Science, Business Administration or related fieldRelevant professional certifications (e.g., CSPO, SAFe POPM, AWS ML Specialty, Google Professional ML Engineer)Why join us
We're on a journey to become market leaders in automotive technology and with that comes incredible opportunities to shape the future of AI and machine learning in automotive retail. Collaborate and learn from industry experts, Data Scientists, and ML Engineers from all over the globe. Work with cutting-edge AI products like VEGA and build LLM-powered agents that directly impact how thousands of automotive retailers operate their businesses. Get the training and support you need to explore the latest in generative AI, agent technologies, MLOps practices, and pioneer new approaches to embedding intelligence into enterprise SaaS solutions.
Join Keyloop and progress your career at the intersection of AI, machine learning, and product ownership, your way.
An inclusive environment to thrive
We're committed to fostering an inclusive work environment. One that respects all dimensions of diversity. We promote an inclusive culture within our business, and we celebrate different employees and lifestyles – not just on key days, but every day.
Be rewarded for your efforts
We believe people should be paid based on their performance, so our pay and benefits reflect this and are designed to attract the very best talent. We encourage everyone in our organisation to explore opportunities which enable them to grow their career through investment in their development but equally by working in a culture which fosters support and unbridled collaboration.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Skills Required
AI orchestration tools, data pipelines, data engineering for AI, vector databases, model serving infrastructure, prompt engineering, AI product success metrics, MLOps best practices, MLOps practices, cloud-based AI ML platforms, LLM-powered applications, generative AI products, modern AI ML technology stack, ML frameworks, feature engineering, AI ML products, machine learning models