LLM Application Engineer (AI)

TL;DR

LLM Application Engineer (AI) (Python/LLMs): Build and ship AI-powered applications and agent workflows for a proactive smart assistant with an accent on reliable reasoning, persistent context, tool use, and real-world task completion. Focus on designing orchestration pipelines, evaluating model quality, integrating APIs and databases, and optimizing production systems for quality, latency, and cost.

Location: Hybrid in the United Kingdom, with the office address in London, Greater London.

Company

hirify.global’s A1 Engineering team is building an AI-native smart assistant for conversations, errands, organization, and everyday workflows.

What you will do

• Build and ship LLM-powered applications and AI agent workflows.
• Design reasoning, planning, memory, tool-use, and multi-step execution systems.
• Develop reliable orchestration pipelines that make probabilistic model outputs predictable, observable, and safe.
• Integrate LLMs with APIs, databases, search systems, internal services, and external tools.
• Build evaluation frameworks and datasets to measure quality, reliability, and regressions.
• Improve production AI systems through debugging, observability, experimentation, and optimization for quality, latency, and cost.

Requirements

• Strong software engineering fundamentals and experience building AI-powered applications.
• Hands-on experience with LLMs, generative AI, or agent-based systems.
• Experience designing prompts, workflows, evaluations, or AI behavior.
• Ability to write clean, production-quality Python code.
• Comfort working across model, system, and product abstraction layers.
• Strong problem-solving skills in ambiguous, fast-moving environments, with a bias toward shipping and iteration.

Culture & Benefits

• Work in a product-focused engineering environment connecting AI research, software engineering, and user experience.
• Own problems end-to-end, from understanding user needs to production delivery.
• Build systems designed for high reliability, scalability, observability, and maintainability.
• Contribute to continuous experimentation and systematic improvement of AI quality.