Top Machine Learning Jobs in Wellington

Leads ML and platform engineering teams building scalable, production-grade machine learning infrastructure. Responsibilities include coaching engineers, guiding architecture and engineering trade-offs, improving MLOps maturity and observability, translating business goals into delivery plans, managing roadmaps, maintaining system health, and supporting production incidents through code review, debugging, or hands-on development. The role supports AI products including conversational GenAI, automated bank reconciliation, document capture, and cash-flow forecasting.

Designs, builds, deploys, and supports AWS cloud environments for diverse customers. The role focuses on infrastructure automation, Infrastructure as Code, DevOps, observability, security governance, incident and problem management, cloud optimization, and continuous service improvement. Responsibilities include advising customers, enabling cloud adoption, supporting mission-critical production environments, contributing to platform strategy, and participating in an after-hours on-call roster.

Lead product strategy and execution for the reconciliation review experience (Auto Bank Rec). Define roadmap, success metrics, and evaluation frameworks; partner with engineering, ML, and UX to shift to management-by-exception; build dashboards and data-driven insights; and expand reconciliation into AI-native, conversational surfaces to accelerate time-to-value.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead pre-sales discovery, solution design, estimation and client workshops; define high-level application architectures, mitigate delivery risks, and incorporate AI/ML and generative AI into enterprise solutions. Produce technical proposals, estimates, and reusable consulting frameworks while guiding senior stakeholders and ensuring solutions deliver business outcomes.