MLOPs Engineer

Team: Artificial Intelligence

Location: Auckland

Commitment: Full-time

Workplace Type: onsite

Key Responsibilities

• MLOps Infrastructure & Platform Enablement
• Design and implement scalable MLOps infrastructure to support model development, training, evaluation, and deployment.
• Build reusable automation frameworks for model lifecycle management, including CI/CT for Large Language Models.
• Establish best practices for reproducible experimentation and production-grade AI system operations.


• Data Foundation for Model Development
• Develop and maintain robust data pipelines and storage foundations required for machine learning and LLM workflows.
• Ensure high-quality, well-governed datasets are available for training, fine-tuning, and benchmarking.
• Partner with Data and AI teams to enable dataset versioning, lineage, and repeatable refresh processes.
• Implement controls for privacy, anonymisation, and compliance when handling enterprise or client-derived training data.


• AWS-Based Model Operations
• Own the deployment and scaling of AI infrastructure on AWS, leveraging services such as S3, ECS/EKS, Lambda, SQS, and CloudWatch.
• Experience in other AWS backed settings on enabling and managing GPU clusters and distributed inference.
• Optimise training and inference environments for performance, reliability, and cost efficiency.
• Implement monitoring, alerting, and operational workflows for model-serving systems.


• Model Deployment & Production Readiness
• Support the deployment of machine learning and LLM models into production environments using modern MLOps practices.
• Collaborate with backend engineering teams to integrate AI services through APIs and enterprise workflows.
• Ensure model systems meet reliability, latency, and scalability requirements.


• Observability, Governance, and Compliance
• Establish monitoring and evaluation pipelines for model performance, drift detection, and operational health.
• Ensure infrastructure and workflows align with enterprise security requirements and responsible AI governance practices.
• Maintain auditability and documentation across datasets, pipelines, and model releases.


• Knowledge Systems and Graph Integration (Preferred)
• Support AI architectures that incorporate knowledge graphs and graph databases for retrieval, reasoning, and enterprise context enrichment.
• Collaborate with engineering teams to operationalise graph-backed pipelines alongside modern ML systems.
• Contribute to scalable integration patterns between graph data layers and LLM-based applications.

Qualifications

• Required
• Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field.
• 3+ years of experience in MLOps, ML infrastructure, or cloud-based data/AI platform engineering.
• Strong hands-on experience building and operating AI infrastructure on AWS.
• Experience developing data foundations and pipelines supporting model development workflows.
• Familiarity with containerisation and orchestration tools such as Docker and Kubernetes.
• Demonstrated ability to support ML systems moving from experimentation into production environments.


• Preferred
• Experience in enterprise software, legal tech, or other regulated domains.
• Familiarity with graph databases (e.g., Stardog) and knowledge graph-based AI architectures.
• Exposure to LLM pipelines, retrieval-augmented generation (RAG), or agent-based AI workflows.
• Experience with Infrastructure-as-Code tools such as Terraform or CloudFormation.

Success Metrics

• Reliable and scalable AWS-based infrastructure enabling efficient model development and deployment.
• Strong data foundations supporting compliant, repeatable training and evaluation workflows.
• Reduced friction in research-to-production transitions for AI engineering teams.
• High operational quality through monitoring, governance, and automation of ML systems.
• Successful enablement of advanced AI architectures, including graph-backed and retrieval-driven workflows.