AI Careers
Landscape.
The roles shaping AI engineering in 2026 — what they do, what skills they need, and why they matter. Whether you're pivoting into AI or levelling up, this is the map.
AI Engineer
Builds production applications on top of foundation models. The bridge between raw model capabilities and shipped products — owns the full stack from API integration to deployment.
“Didn't exist 3 years ago, now one of the most posted engineering titles globally.”
LLM Engineer
Deep specialist in large language models — fine-tuning, alignment, evaluation, and optimisation. Works closer to the model layer than an AI Engineer.
“As companies move beyond GPT wrappers, they need engineers who understand models at a deeper level.”
MLOps Engineer
Keeps ML systems running in production. The DevOps of the machine learning world — pipelines, monitoring, scaling, and reliability.
“Every team that's shipped a model needs someone keeping it alive in production.”
AI Product Manager
Owns the roadmap for AI-native products. Needs enough technical depth to work with LLM engineers, enough product sense to ship things users love.
“Traditional PMs struggle with AI's probabilistic outputs. Companies pay a premium for PMs who get both.”
Data Scientist
Classic data science, now expected to work fluently with LLMs and generative AI. Covers analysis, statistical modelling, and increasingly LLM integration.
“High supply but strong demand for those who blend traditional stats with modern AI tools.”
AI Solutions Architect
Designs enterprise-scale AI systems. Translates business requirements into architecture — which models, which infra, which integrations, and how it all holds together.
“Enterprises are spending big on AI but can't figure out how to implement it safely at scale.”
Computer Vision Engineer
Builds systems that understand images and video — object detection, classification, segmentation. Highly specialised and well-compensated across multiple industries.
“Manufacturing, retail, healthcare, and security all have active CV use cases with real budget.”
RAG / Search Engineer
Specialises in retrieval-augmented generation — the systems that give LLMs access to your private data. Combines search engineering with LLM integration.
“Every company with a knowledge base wants to chat with their data. RAG is how you do it properly.”
AI-Augmented Full Stack Developer
Full stack engineer who ships significantly faster using AI coding tools. Not a new role — an evolved expectation of every developer entering the market today.
“The bar for full stack has shifted. Developers not fluent with AI tools are falling behind.”
Prompt Engineer
Designs, tests, and optimises the instructions that drive AI systems. Sits at the intersection of linguistics, product thinking, and engineering.
“Underhyped as a standalone title, but the skills are embedded in almost every AI role now.”
Meet people in these roles
The Code, Coffee & AI community includes engineers across all of these roles. Join us at the next event to connect, share notes, and learn from people doing this work in Auckland.