Purpose of position:
Design, build, and deploy production AI/ML systems — including model development, data pipelines, API services, and automation tooling — that directly support our insurance products and internal operations.
Responsibilities:
AI/ML system development
Design, train, evaluate, and deploy machine learning models for pricing, fraud detection, customer segmentation, and operational automation
Build and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, validation, and serving
Implement model monitoring and retraining workflows to ensure sustained performance in production
Write clean, tested, production-grade Python code; leverage Rust where performance is critical
Platform & infrastructure
Develop and maintain internal tooling, APIs, and microservices that expose AI capabilities to downstream systems
Architect scalable data processing pipelines using modern orchestration and compute frameworks
Own deployment infrastructure: containerisation, CI/CD, and observability for ML services
Integrate with internal platforms including Genesys Cloud, WhatsApp channels, and rating engines where AI capabilities are required
Data engineering & analysis
Write performant SQL and Python to extract, transform, and analyse large datasets
Build dashboards and automated reporting to quantify model performance and business impact
Collaborate with actuarial and product teams to translate business problems into tractable modelling tasks
Research & continuous improvement
Evaluate emerging techniques (LLMs, generative AI, reinforcement learning) for applicability to business problems
Prototype rapidly; validate or kill ideas quickly with structured experiments
Contribute to internal knowledge sharing through code reviews, technical write-ups, and workshops
Quality & reliability
Write unit and integration tests for all production code
Conduct rigorous model validation: out-of-sample testing, fairness audits, and sensitivity analysis
Maintain reproducibility through version-controlled experiments, data snapshots, and clear documentation
Requirements:
BEng (Mechanical, Computer, Electronic) - or equivalent quantitative degree
Master's degree in a related field (advantageous)
2 - 5 years' professional experience building and deploying ML/AI systems
Strong proficiency in Python (Polars, scikit-learn, PyTorch or TensorFlow)
Experience with SQL at an intermediate-to-advanced level
Familiarity with Rust, C++, or another systems language (advantageous)
Hands-on experience with cloud platforms (Azure, AWS, or GCP) and containerisation (Docker, Kubernetes)
Solid grounding in linear algebra, probability, optimisation, and statistical inference
Experience with version control (Git), CI/CD pipelines, and software engineering best practices
Skills and Attributes:
Solves problems independently; does not wait to be told what to do
Writes clear, maintainable code — treats engineering rigour as non-negotiable
Communicates technical trade-offs concisely to both engineers and non-technical stakeholders
Comfortable with ambiguity; able to scope and decompose ill-defined problems without hand-holding
High standard of output — ships work that is correct, tested, and documented
Intellectually honest: flags uncertainty, quantifies confidence, and knows when to escalate
