Responsibilities

Provide technical leadership in the architecture, development, and deployment of production-grade machine learning solutions for geospatial applications.
Architect scalable machine learning systems and partner with software engineers to integrate AI components into end-to-end platforms.
Own model performance and system reliability by driving best practices in MLOps, deployment, monitoring, and continuous improvement.
Work closely with product, business development, and sales teams to translate business objectives into well-defined technical requirements and delivery plans.
Mentor engineers and guide technical decisions, while providing technical support for pre- and post-sales activities as required.

Requirements

Knowledge in Computer Science, Electrical Engineering, or a related discipline.
At least 5 years of relevant experience delivering production machine learning solutions.
Strong foundation in machine learning and deep learning, including model development, evaluation, and optimization.
Proficient in Python, with hands-on experience in common ML frameworks (e.g., PyTorch, scikit-learn) and computer vision libraries (e.g., OpenCV).
Experience with deploying and operating ML services, including API development (e.g., Flask / FastAPI / Node.JS) and containerisation (e.g. Docker).
Experience with large language models, retrieval-augmented generation, retrieval optimization, and graph databases is an advantage.
Self-motivated and proactive, with strong ownership, stakeholder management, and the ability to mentor and work effectively across teams.