Applied AI Engineer

About the role

We are looking for an Applied AI Engineer to join an innovative team building AI-powered products designed to make everyday digital tasks significantly easier.

Our client is developing a proactive AI assistant capable of helping users with conversations, planning, organisation and multi-step workflows — with as little manual prompting as possible.

The core challenge is not simply building impressive AI demos. The goal is to create reliable, production-ready AI systems that can maintain context, reason through complex tasks, interact with external tools and deliver consistent results despite the inherent variability of modern language models.

What you'll do

In this role, you'll work at the intersection of AI, software engineering and product development. You'll take ownership of AI problems from the initial idea through implementation and deployment, turning model capabilities into features that work reliably for real users.

Your responsibilities will include:

• Building and deploying AI-powered product features from model layer through to the user experience
• Designing and improving prompts, tools, memory systems and agent workflows
• Converting LLM outputs into structured and dependable application behaviour
• Investigating and resolving issues across models, orchestration, infrastructure and product layers
• Improving system performance with a focus on latency, cost and reliability
• Creating practical evaluation methods to measure how AI systems perform in real-world scenarios
• Working closely with product and engineering teams to turn open-ended challenges into robust solutions

Tech stack

• Python
• PyTorch / JAX
• LLMs — including OpenAI-compatible APIs, LLaMA, Qwen and similar models
• Inference & model serving — e.g. vLLM
• Vector databases

How the team works

The team is intentionally small and highly skilled, with an emphasis on moving quickly while maintaining a high standard of quality.

You should be comfortable working with a significant degree of independence, bringing structure to ambiguous problems, making sound technical decisions and taking ownership of your work from idea to production.

The culture combines fast execution, collective decision-making and continuous learning. The ultimate goal is to build an AI product that delivers genuinely useful experiences to people at global scale.

What success looks like

In this role, you'll be expected to help ensure that:

• ML models running in production meet defined accuracy, latency and reliability goals
• Production problems are detected, investigated and resolved efficiently
• Data pipelines, training processes and inference infrastructure remain robust and maintainable
• AI-powered features are delivered effectively in collaboration with engineering, product and research teams
• Model and system improvements are driven by real-world usage and measurable results

You will be a strong match if you have:

• A solid understanding of machine learning and modern neural network architectures
• Practical experience training, fine-tuning or deploying machine learning models
• Strong programming skills and the ability to produce clean, production-ready code
• The ability to move comfortably between different layers of the stack — from models and infrastructure to the final product
• Strong analytical and problem-solving skills, particularly when working with complex or loosely defined problems
• A hands-on mindset and a genuine focus on delivering, testing and continuously improving solutions