Data and AI Analyst

Role overview

This position is for a practical AI technical consultant who will help design and implement production-ready AI and Generative AI solutions. The work is strongly engineering-focused, with emphasis on data pipelines, scalable cloud platforms, and reliable integration of AI capabilities into enterprise systems. The role will be performed under the direction of senior architects and technical leads.

The right candidate should have a solid base in AI/ML engineering and data engineering, along with the ability to build, test, and deploy AI components in enterprise settings. You will collaborate with experienced engineers and architects to deliver AI systems that are dependable, scalable, and aligned with business objectives. The expected background is 2 to 4 years of professional experience in AI/ML, data engineering, or advanced analytics engineering.

Responsibilities

• Develop AI and Generative AI modules according to the agreed architecture and design direction.
• Create and support data pipelines and feature engineering processes for AI use cases.
• Help build and connect LLM and Generative AI capabilities into applications and platforms.
• Design workflows for model training, assessment, and inference.
• Assist with the creation of scalable AI services and APIs.
• Work on vector search, embeddings pipelines, and retrieval-based patterns such as RAG.
• Support MLOps and LLMOps activities, including testing, deployment, and monitoring.
• Investigate and resolve issues related to performance, data quality, and model integration.
• Partner with data engineers, ML engineers, and solution architects to deliver AI functionality.
• Help produce technical documentation and implementation standards.

Requirements

• Practical experience using Python for AI/ML and data engineering tasks.
• Working familiarity with machine learning and deep learning frameworks such as PyTorch, TensorFlow, or similar tools.
• Ability to build data pipelines and data processing workflows.
• Exposure to Generative AI and LLM integration approaches.
• Useful knowledge of embeddings, vector databases, or retrieval pipelines.
• Experience integrating AI services through APIs.
• Exposure to cloud AI services on Azure, AWS, or GCP.
• Understanding of the AI model lifecycle, including evaluation and deployment basics.
• Awareness of containerization and CI/CD concepts.
• Strong analytical thinking and comfort working in fast-paced technical teams.

Additional information

This role is based in Auckland, Auckland, New Zealand, and is structured as a full-time onsite position. No salary, stipend, opening count, start date, or application deadline was specified in the source. The job is not described as an internship, so internship duration does not apply.

Who can apply

Candidates with 2 to 4 years of professional experience in AI/ML, data engineering, or advanced analytics engineering roles are the intended fit for this opportunity.