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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 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. 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 Originally posted on Himalayas