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LLM Application Engineer

Bjak · United Kingdom

On-sitePosted 12 Aug 2026

What this role requires

One requirement, read out of the advert rather than guessed from the job title:

Also mentioned, not required: LLM APIs, OpenAI, PyTorch, JAX. Worth having, but their absence is not what gets a CV filtered out.

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Job description

About A1

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

About the Role

As an LLM Application Engineer, you will build the intelligence layer that powers A1's AI experiences.

You will work at the intersection of LLMs, software engineering, and product - designing agent workflows, improving model behaviour, and turning AI capabilities into reliable user experiences.

Read the full description (34 more sections)

You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation system and continuously improving AI behaviour in production.

Focus

Build and ship LLM-powered applications and AI agent workflows

Design systems for reasoning, planning, memory, tool uuse and multi-step execution

Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions

Integrate LLMs with APIs, databases, search, internal services, and external tools.

Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour

Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions

Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX

Optimise AI systems for quality, latency, and cost

Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions

Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement

Tech Stack

Python

LLM APIs and model providers, including OpenAI-compatible APIs and open-weight models

Agent frameworks and orchestration systems

Vector databases and retrieval systems

Backend services, APIs, and distributed systems

PyTorch / JAX

Ideal Experience

Strong software engineering fundamentals with experience building AI-powered applications

Hands-on experience with LLMs, generative AI, or agent-based systems

Experience designing prompts, workflows, evaluations, or AI behaviour

Ability to write clean, production-quality code

Comfortable working across abstraction layers (model → system → product)

Strong problem-solving skills in ambiguous, fast-moving environments

Bias toward shipping, iteration, and continuous improvement

Outcomes

AI features reach production quickly and deliver measurable user impact

LLM-powered workflows are reliable, scalable, observable, and maintainable

AI quality improves through systematic evaluation, experimentation, and iteration

AI workflows become increasingly predictable, efficient, and cost-effective

Complex AI capabilities are translated into simple, intuitive user experiences

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