Applied AI Engineer

Project description About the Project: There are over 5 billion users using basic applications today such as email, notes, and tasks that are not AI-native. Our client's mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising, and workflows, with minimal prompting. Their platform 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. Their platform objective is to help users complete tasks daily in an enjoyable way with over ~90% reduced time. Technical requirements - Python - PyTorch / JAX - LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) - Inference / serving (e.g., vLLM) - Vector DB Responsibilities As an Applied AI Engineer, you will turn model capabilities into real product behavior. You will own problems end-to-end, from shaping model behavior to building the systems around it, to ensuring it performs reliably in production. This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users in real-world usage. - Build and ship AI features end-to-end (model → system → user experience). - Design and iterate on prompts, tools, memory, and agent workflows. - Turn raw model outputs into structured, reliable, and predictable behaviors. - Debug issues across the full stack (model, orchestration, infra, UX). - Optimize for latency, cost, and production reliability. - Develop lightweight evaluation frameworks to measure real-world performance. - Work closely with product and engineering to translate ambiguous problems into working systems. - Ensure ML models in production meet expected accuracy, latency, and reliability targets. - Maintain robust, reproducible, and maintainable data pipelines, training loops, and inference systems. Must have - Strong foundation in machine learning and modern neural network architectures. - Hands-on experience with training, fine-tuning, or deploying ML models. - Ability to write clean, production-quality code. - Comfort working across abstraction layers (model → infra → product). - Strong problem-solving skills in ambiguous, fast-moving environments. - Bias toward shipping, iteration, and continuous improvement. Recruitment process Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. Please note that due to the volume of applications, we will only contact selected candidates. Got questions? To learn more details about this job contact Joanna at jgrabowska@maximaeurope.com