AI Engineer at King Price Insurance

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