Applied AI Engineer

Applied AI Engineer

Location: Remote or onsite

Employment type: Full-time

About the role

We’re hiring an Applied AI Engineer to develop and deploy AI systems that power product features and internal capabilities.

You’ll work across machine learning, natural language processing, data, experimentation, and production engineering. The role involves turning research and model capabilities into systems that can be tested, measured, deployed, and improved using real-world data.

You’ll work with product, engineering, and design teams from problem definition through production.

What you’ll do

• Design, build, test, and deploy machine-learning and AI systems.
• Develop models and algorithms for structured and unstructured data.
• Build prototypes to test whether an AI approach can solve a defined product or business problem.
• Prepare, analyse, and evaluate datasets used for model development.
• Define evaluation methods and metrics for AI-powered features.
• Improve model quality, latency, reliability, and infrastructure cost.
• Integrate models into production applications and backend services.
• Monitor deployed systems and investigate model or data failures.
• Compare models, architectures, and approaches using experiments and production data.
• Work with product and engineering teams to define technical requirements and trade-offs.
• Review code and contribute to shared AI and engineering standards.
• Document models, datasets, experiments, and production behaviour.

What we’re looking for

• Experience developing machine-learning, deep-learning, NLP, or applied AI systems.
• Strong programming experience with Python.
• Experience with PyTorch, TensorFlow, or equivalent machine-learning frameworks.
• Experience analysing data using tools such as Pandas and NumPy.
• Understanding of machine-learning algorithms, model evaluation, and experimentation.
• Experience working with structured and unstructured datasets.
• Understanding of software engineering principles required to move models into production.
• Ability to identify and investigate model failure modes.
• Ability to explain technical results and trade-offs to engineering, product, and other colleagues.

A degree in Computer Science, Machine Learning, AI, Data Science, or a related subject can be useful, but equivalent practical experience is also accepted.

Nice to have

• Experience deploying machine-learning models in production.
• Experience with large language models, embeddings, or multimodal models.
• Experience developing evaluation datasets and automated evaluation pipelines.
• Experience with retrieval, ranking, recommendation, or classification systems.
• Experience using AWS, Azure, or Google Cloud for machine-learning workloads.
• Experience with model monitoring, experiment tracking, or ML infrastructure.
• Experience building APIs or services around machine-learning models.
• Experience mentoring engineers or leading technical projects.

What a typical week may include

You might spend your time:

• Testing whether a model can solve a new product problem.
• Preparing or analysing a dataset for an experiment.
• Comparing models against defined quality and performance metrics.
• Building a prototype and working with engineers to move it into production.
• Investigating why a deployed model is producing incorrect or inconsistent results.
• Reviewing production data to identify opportunities for improvement.
• Running experiments to improve model quality, latency, or cost.
• Reviewing code or experiment results with other engineers.
• Presenting findings and technical trade-offs to product and engineering teams.

How we work

AI projects start with a defined problem and measurable criteria for deciding whether an approach works.

We use experiments, evaluation datasets, and production behaviour to choose between models and technical approaches.

Engineers are expected to own work from initial experimentation through deployment and monitoring, while collaborating with product, backend, client, and other AI engineers.

Hiring approach

You do not need to match every item in this description to apply. We assess candidates based on the skills required to do the job and the experience they can bring to the team.

We welcome applications regardless of gender, race, ethnicity, religion, disability, sexual orientation, age, or background.