Applied AI Engineer at Unframe

<p><strong>About Us</strong></p> <p>At Unframe, we build AI-powered products that address real-world challenges. Our platform team is at the core of this mission, developing the infrastructure, reusable components, and internal tools that enable rapid and reliable product delivery. We work on-site from our Tel Aviv office, collaborating closely across engineering, product, and AI to move fast and build with purpose.</p> <p><strong>What You'll Do</strong></p> <p>As an Applied AI Engineer, you’ll work closely with customers and internal teams to translate business problems into AI-driven solutions. You’ll be hands-on with AI workflows, and help bridge the gap between model capabilities and real-world impact.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Implement workflows and configure model behavior for customer solutions.</li> <li>Build integrations between AI logic and deterministic building blocks.</li> <li>Work with platform and research teams to surface platform-level improvements.</li> </ul> <p><strong>Requirements</strong></p> <ul> <li>Bachelor’s degree in Computer Science, Engineering, or a related field.</li> <li>4+ years of experience in software engineering, data science, machine learning, or research roles (including relevant academic experience).</li> <li>Experience building and delivering production solutions to customers using Python, TypeScript, or similar.</li> <li>Experience building pipelines for structured and unstructured data, including work with vector databases and retrieval-augmented generation (RAG)-like architectures.</li> <li>Experience with prompt design, evaluation, and iteration workflows.</li> <li>Ability to rapidly understand complex domains and read and implement research papers.</li> <li>Comfortable working with real-time applications and production systems.</li> </ul> <p><strong>Nice to Have</strong></p> <ul> <li>Master’s degree or PhD in AI, Computer Science, or a related technical field.</li> <li>Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Google ADK) and patterns such as ReAct, self-reflection, and hierarchical delegation.</li> <li>Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.</li> <li>Background in data pipelines, APIs, or system integrations.</li> <li>Experience working with enterprise or B2B customers.</li> <li>Experience working in fast-paced startup environments.</li> </ul>