Machine Learning Engineer - Agentic AI

Posted on:

October 29, 2025

Job description

Job Description

The Machine Learning (ML) Engineer will undertake technical work to design, prototype and develop LLM-based products that utilise Earth Observation data, primarily Nearmap imagery and AI data. Where a data scientist will typically focus more on the meaning in the data, and producing accurate models, the ML Engineer focusses on integrating models and data into complex workflows that autonomously solve problems. The role requires solving a variety of challenging problems in software engineering, agentic AI design and evaluation, and close collaboration with data scientists as a peer.

Responsibilities

Key Responsibilities

Performs software engineering tasks required by an end-to-end AI system.
Designing, building and maintaining subcomponents of an AI system, in collaboration with data scientists: microservices, APIs, working with technologies such as Docker, Kubernetes, MCP.
Collaborating with other engineering teams and DevOps to ensure consistent best practices and integration of systems.
Reviews code and work of peers.

Job requirements

Personal Attributes we love to see:

Pragmatism: While extensive knowledge of ML theory is highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work.
Collaboration: We believe data science is a team sport, and are after candidates who can communicate well, share knowledge, and be open to taking on ideas from anyone in the team. Having worked on shared code-bases in a commercial environment is a big plus, but it's the attitude that matters most.
Technical Skills: A decent base of python and linux are key to a role in the team. Other than that, we're pretty flexible - we know tools are changing rapidly, and will continue to do so for many years to come. Experience with tools like Kubernetes, Helm, PyTorch, Terraform, Prometheus etc. are highly valued, but not mandatory.
Attention to detail: Showing attention to detail when it counts is important.

Qualifications

Key Requirements

Formal education in a technical, data related field (Bachelor’s degree in computer science, engineering, statistics, physics, etc.), with an emphasis on software development.
Ideally at least 2 years experience writing production grade commercial software in a team environment.
Machine learning knowledge and experience with LLM-powered systems is highly desirable, but a passion to learn more is sufficient. We see ML engineering as a sub-field of software engineering, that benefits greatly from a good
Mandatory
- Programming/Tech Environments: Ability to code in scientific python, using a linux environment, and git for source control.
- Machine Learning: Appreciation of machine learning fundamentals.
- Engineering Approach: Follow best practices in modern software engineering, applying them to build robust, scalable machine learning systems.
Highly Desirable
- Domain Knowledge – NLP/LLM: Working on Machine Learning problems applied to text data.
- Software Engineering: Working on shared codebases to produce production quality code.
- Cloud Computing: Working on AWS or GCP using distributed virtual machines, docker containers, etc.
- GPU: Using GPUs to accelerate scientific computing.
- Deep Learning: Applying modern artificial neural networks to solve machine learning problems.
Scale: Working with large data sets, where data sets don’t fit into memory, and require multiple nodes to compute efficiently.