Lead Applied AI Engineer (Research & Development)

The Role

Core Profile

• Build, train, validate, and calibrate models with a strong understanding of the underlying mathematics.
• Understand foundation models, agents, and emerging AI techniques and translate research into working products.
• Work across backend services, data infrastructure, and frontend applications.
• Bring strong product instincts and an appreciation for intuitive, polished user experiences.

Responsibilities

• Lead R&D for intelligent systems capable of sophisticated analysis, reasoning, and decision-making.
• Create knowledge systems that ingest multiple internal sources and produce structured organisational intelligence.
• Move ideas from research and prototypes into production across models, infrastructure, services, and interfaces.
• Define architecture and the strategic roadmap for AI and R&D initiatives.
• Apply emerging approaches in foundation models, causal inference, streaming analytics, and real-time machine learning.
• Mentor and develop a high-performing technical team.

Required Qualifications

• At least 6 years of engineering experience in AI or data-intensive products.
• Production experience building and deploying AI and machine learning models.
• Strong Python expertise.
• Experience with Spark, Kafka, Snowflake, Databricks, or similar distributed data technologies.
• Experience with PyTorch, TensorFlow, or similar ML frameworks.
• Experience architecting and scaling production-grade systems.
• Ability to build product-facing applications and interfaces.
• Experience leading 0-to-1 R&D initiatives.

Preferred Qualifications

• A PhD in AI, machine learning, computer science, or a related discipline is strongly preferred, with equivalent experience considered.
• Published AI or machine learning research or open-source AI or data contributions.
• Experience in financial services, regulated data-intensive environments, real-time decisioning, or financial APIs.

Company Context