LLM Application Engineer

Role Summary

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.

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

Requirements

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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