ActAI
LLM Application Engineer
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About ActAI
There are over 5 billion users using basic applications today such as email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
About the Role
As an LLM Application Engineer, you will build the intelligence layer that powers ActAI'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.
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Focus
- Build and ship LLM-powered applications and AI agent workflows
- Design systems for reasoning, planning, memory, tool use 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
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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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