Architecting and deploying scalable products across major cloud platforms (AWS, GCP, or Azure)
Designing and optimising ML workflows for predictive modelling, anomaly detection, and decision systems
Building and integrating multi-modal LLM applications (text, image, structured and sensor data inputs)
Developing robust data pipelines, model deployment processes, and monitoring frameworks
Applying emerging AI approaches such as Retrieval-Augmented Generation (RAG) and advanced context orchestration frameworks
Owning the full lifecycle from rapid prototyping through to production-grade deployment
Collaborating closely with founders on product direction and long-term technical strategy

Degree in Computer Science, Machine Learning, Engineering, or similar technical field (or equivalent experience)
Proven experience building and shipping cloud-native products
Strong grounding in machine learning workflows, particularly predictive modelling and anomaly detection
Experience with MLOps, model deployment, and monitoring in production environments
Hands-on exposure to multi-modal LLM systems
Understanding of reinforcement learning and modern AI system design patterns
A startup mindset — comfortable with ambiguity, adaptable, and execution-focused
Ability to balance pragmatic experimentation with building scalable, reliable systems

Equity Upside: Meaningful equity potential alongside base salary
Autonomy: High ownership, high trust, and direct influence over product and strategy
Growth Trajectory: Significant scope to grow into a senior technical leadership position
Founding Impact: Your technical decisions will define the company’s architecture and engineering culture

AI / Machine Learning Engineer – Reinforcement Learning, Decision Systems

Key skills