AI Engineer

🚀 Applied AI Engineer | London | AI-Native Product

We’re working with an ambitious, early-stage AI company building a new generation of proactive AI assistants for everyday users.

The product is focused on making AI genuinely useful in the real world — handling conversations, tasks, errands and workflows with minimal prompting. The team is tackling challenging problems around long-running workflows, persistent context, multi-step reasoning, tool use and reliable task completion.

The Role

As an Applied AI Engineer, you’ll turn the latest model capabilities into reliable product experiences.

You’ll work across machine learning, AI systems and product engineering, owning problems end-to-end — from shaping model behaviour and building agent workflows through to deploying, evaluating and improving them in production.

This is a hands-on role for someone who enjoys moving quickly, working through ambiguity and making AI systems actually work for users rather than simply building demos.

What You’ll Do

• Build and ship AI features end-to-end, from model to system to user experience
• Design and iterate on prompts, tools, memory and agent workflows
• Turn raw LLM outputs into structured, reliable and predictable behaviours
• Debug issues across models, orchestration, infrastructure and product
• Optimise AI systems for latency, cost and production reliability
• Build lightweight evaluation frameworks to measure real-world AI performance
• Develop and improve data pipelines, training workflows and inference systems
• Work closely with product and engineering teams to turn ambiguous problems into working solutions
• Continuously iterate based on real-world usage and failure modes

Tech Stack

• Python
• PyTorch / JAX
• Large Language Models — OpenAI-style APIs, LLaMA, Qwen and similar
• vLLM / model serving
• Vector databases
• Cloud and production ML infrastructure

What We’re Looking For

• Strong foundations in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning or deploying ML models
• Strong production-quality Python engineering skills
• Experience working across multiple abstraction layers — model → infrastructure → product
• Understanding of LLM applications, agents, RAG or similar AI systems
• Strong problem-solving ability in ambiguous and fast-moving environments
• A practical, hands-on approach to engineering
• Bias toward shipping, experimentation and continuous improvement
• Interest in building AI systems that are reliable in real-world usage

What You’ll Own

You’ll help ensure that AI systems:

• Perform reliably in production
• Meet accuracy, latency and reliability targets
• Can be evaluated and improved using real-world signals
• Have robust data, training and inference pipelines
• Handle failures and unexpected model behaviour effectively
• Deliver measurable improvements to the user experience

Why Join?

You’ll be joining a high-calibre, hands-on AI team working on genuinely difficult problems at the intersection of AI, product and systems engineering.

The team values speed, technical judgement, ownership and a willingness to learn. You’ll have significant autonomy and the opportunity to work directly on systems that could reach a very large user base.

Interview Process

The process is designed to be efficient, with 3–4 interviews for candidates who progress.

If you’re excited about building AI systems that move beyond chat and actually complete useful tasks for people, I’d be very interested in hearing from you.

📩 Apply directly or message me for more information.