We’re partnering with a well-backed, product-led SaaS company who are building AI capability directly into their platform. They’ve already delivered an AI application into customer trial environments and now need an MLOps / ML Platform Engineer to take ownership of
deploying, scaling and operating it properly in production.
This is a hands-on role, ideal for someone with strong AWS infrastructure experience who wants to specialise further into AI platform operations.
What you’ll be doing You’ll design, deploy and operate the AI infrastructure that supports customer-facing AI features, including:
- Productionising AI services and making them scalable, secure and reliable
- Supporting and improving AWS Bedrock deployments
- Building and improving MLOps workflows (deployment pipelines, monitoring, operational tooling)
- Working closely with AI developers and platform/DevOps engineers to enable AI delivery
- Ensuring infrastructure and operational processes meet strict compliance expectations
- Contributing best practice and uplift across the wider DevOps team
What we’re looking for This role suits someone who is
intermediate to senior, practical, and comfortable taking ownership.
Key experience:
- Strong AWS background (cloud infrastructure + operations)
- Experience deploying and running AI/ML workloads in production (LLMs / RAG / inference services etc.)
- Comfortable with DevOps fundamentals: CI/CD, automation, monitoring, incident response
- Solid experience with containers (Docker) and modern deployment patterns (ECS/EKS/serverless)
- Great communication skills, this team is distributed and highly collaborative
Nice to have (not required): - AWS Bedrock exposure
- Experience in regulated environments / audit-heavy organisations
- MLOps tooling (SageMaker Pipelines, MLflow, Kubeflow etc.)
- Security-first mindset (IAM, logging, encryption, least privilege)
Why this role? - Real ownership, this is a newly created role, not a replacement
- Interesting technical challenge: AI in production, not experimentation
- Flexible working (Wellington office available, hybrid supported OR fully remote)
- Strong internal focus on modern AI tooling and innovation
