We’ve got a position available for an AI Engineer
Company: Porcupine Union
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
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Design, train, evaluate, and deploy machine learning models for pricing, fraud detection, customer segmentation, and operational automation
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Build and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, validation, and serving
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Implement model monitoring and retraining workflows to ensure sustained performance in production
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Write clean, tested, production-grade Python code; leverage Rust where performance is critical
Platform & infrastructure
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Develop and maintain internal tooling, APIs, and microservices that expose AI capabilities to downstream systems
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Architect scalable data processing pipelines using modern orchestration and compute frameworks
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Own deployment infrastructure: containerisation, CI/CD, and observability for ML services
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Integrate with internal platforms including Genesys Cloud, WhatsApp channels, and rating engines where AI capabilities are required
Data engineering & analysis
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Write performant SQL and Python to extract, transform, and analyse large datasets
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Build dashboards and automated reporting to quantify model performance and business impact
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Collaborate with actuarial and product teams to translate business problems into tractable modelling tasks
Research & continuous improvement
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Evaluate emerging techniques (LLMs, generative AI, reinforcement learning) for applicability to business problems
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Prototype rapidly; validate or kill ideas quickly with structured experiments
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Contribute to internal knowledge sharing through code reviews, technical write-ups, and workshops
Quality & reliability
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Write unit and integration tests for all production code
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Conduct rigorous model validation: out-of-sample testing, fairness audits, and sensitivity analysis
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Maintain reproducibility through version-controlled experiments, data snapshots, and clear documentation
Requirements:
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BEng (Mechanical, Computer, Electronic) - or equivalent quantitative degree
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Master’s degree in a related field (advantageous)
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2–5 years’ professional experience building and deploying ML/AI systems
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Strong proficiency in Python (Polars, scikit-learn, PyTorch or TensorFlow)
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Experience with SQL at an intermediate-to-advanced level
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Familiarity with Rust, C++, or another systems language (advantageous)
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Hands-on experience with cloud platforms (Azure, AWS, or GCP) and containerisation (Docker, Kubernetes)
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Solid grounding in linear algebra, probability, optimisation, and statistical inference
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Experience with version control (Git), CI/CD pipelines, and software engineering best practices
Skills and Attributes:
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Solves problems independently; does not wait to be told what to do
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Writes clear, maintainable code — treats engineering rigour as non-negotiable
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Communicates technical trade-offs concisely to both engineers and non-technical stakeholders
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Comfortable with ambiguity; able to scope and decompose ill-defined problems without hand-holding
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High standard of output — ships work that is correct, tested, and documented
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Intellectually honest: flags uncertainty, quantifies confidence, and knows when to escalate
Should you not receive any feedback within ten (10) working days after the closing date, please accept your application as unsuccessful.
