LLM Application Engineer

About the role

About ActAI

There are over 5 billion users using basic applications today such 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 first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

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.

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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The evidence behind this listing

Workplace

Remote Employer field

Seniority

Not established

Employment type

full time

Location as listed

United States

Pay and work authorization

Stated pay

Not stated

Visa sponsorship

Not stated

Security clearance

Not established

Citizenship

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