Founding Machine Learning Engineer (Recommendations + GenAI)

Whizdom AI is an early-stage AI startup building products around recommendation systems, personalisation, and GenAI agents. The company is a small team working directly on real customer problems, shipping quickly, measuring outcomes, and iterating fast. Everyone is expected to take ownership, improve systems proactively, and help build engineering foundations for scaling.

• 5+ years of experience building and shipping ML systems or intelligent product features
• Strong foundations in machine learning, statistics, computer science, or a related quantitative field
• Strong Python programming skills and experience working with ML workflows
• Experience developing and evaluating machine learning models in production or near-production environments
• Good understanding of model evaluation, feature engineering, experimentation, and data quality challenges
• Experience working with behavioural, transactional, contextual, or large-scale datasets
• Strong software engineering practices, including writing clean, testable, and maintainable code
• Ability to work independently, communicate clearly, and solve ambiguous technical problems
• Upper-Intermediate English level or higher

Nice to have:

• Experience with recommendation systems, ranking, search, personalisation, or marketplace optimisation
• Experience with LLM applications, RAG, GenAI agents, prompt engineering, or GenAI evaluation
• Experience running A/B tests and online experiments
• Experience with real-time ML systems, streaming features, low-latency inference, or online learning
• Experience with causal inference, uplift modelling, multi-armed bandits, or optimisation methods
• Experience with cloud ML infrastructure, containerised deployment, and MLOps workflows
• Experience in iGaming, fintech, e-commerce, or other domains with behavioural and transactional data
• Experience with predictive analytics use cases such as segmentation, churn prediction, LTV modelling, or opportunity prioritisation

• Design, develop, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows
• Translate product goals into ML problems, evaluation approaches, experiments, and production solutions
• Analyse behavioural, transactional, contextual, and unstructured data to identify patterns and improve model performance
• Develop offline evaluation frameworks and support online experiments to measure model quality and business impact
• Improve GenAI workflows through retrieval, context management, prompting, tool usage, orchestration, and evaluation approaches
• Perform error analysis and investigate model limitations to improve reliability and performance
• Collaborate with backend and platform engineers to deploy, monitor, and iterate on ML solutions in production
• Define ML metrics, experimentation practices, and technical standards
• Build reusable ML components and maintain clean, testable Python code
• Support predictive analytics initiatives, including segmentation, churn prediction, opportunity ranking, and other data-driven solutions

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