LLM Application Engineer

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As an LLM Application Engineer at A1 you will build the intelligence layer powering AI experiences, working at the intersection of LLMs, software engineering, and product to design agent workflows and turn AI capabilities into reliable user experiences. The role emphasizes end-to-end ownership, from understanding user needs to integrating models and tools, building evaluation systems, and continuously improving AI behaviour in production. To stand out, demonstrate hands-on work shipping AI apps, building reasoning and memory systems, and creating orchestration pipelines that translate probabilistic outputs into safe actions. In your application, highlight production code quality, cross-functional collaboration with product and engineering, and a track record of reducing latency and cost while increasing reliability. Include projects showing Python, LLM APIs, vector stores, and orchestration platforms.

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.

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