About Calo (more here) Calo is an app providing personalized meal plans for busy people through nutritional algorithms, built with love by chefs, nutritionists, and software engineers. Launched in Bahrain in 2019, we have since expanded to 7 countries, delivered millions of meals to our customers, and are now expanding our mission of making healthy easy to retail and other verticals. We're on a noble mission to make healthy easy. We think this is one of the most important problems to tackle in our world today. We have global ambitions - no small thinking here. Why This Role Matters We are looking for a Full-stack Engineer – AI who will build and ship software using AI coding agents such as Codex and similar tools. You will develop applications for internal productivity and commercial products, leveraging AI-assisted development to accelerate engineering workflows. This role is ideal for engineers who are comfortable working with AI-driven coding environments, orchestrating coding agents, and building modern full-stack applications quickly and reliably. You will work across the entire product stack, from backend systems and APIs to user-facing interfaces, using AI as a core part of the development process. What You’ll Do AI-Assisted Software Development - Use coding agents (e.g., Codex and similar AI tools) to design, implement, and iterate on software systems. - Build and deploy applications using AI-assisted development workflows. - Design prompts, agent workflows, and automation pipelines that improve engineering velocity. Full-stack Development - Develop scalable backend services and APIs. - Build modern frontend interfaces and dashboards. - Integrate internal tools and external services into production systems. AI Engineering Workflow - Design and maintain agent-driven development processes. - Evaluate and adopt emerging AI coding tools and frameworks. - Optimize software delivery through automation and agent orchestration. Product Development - Rapidly prototype and ship features. - Collaborate with product teams to turn ideas into working software. - Build tools that improve internal operations and developer productivity. What We’re Looking For Requirements Experience - 1+ year of professional experience working with AI coding agents (Codex or similar). - Experience building full-stack applications in production environments. Technical Skills Strong experience with: Backend - Node.js / Python / Go / similar modern backend languages - API design (REST / GraphQL) - Databases (PostgreSQL, MongoDB, etc.) Frontend - React / Next.js / modern frontend frameworks - TypeScript / JavaScript AI Development - Working with AI coding assistants or autonomous coding agents - Prompt engineering for development workflows - AI-assisted debugging, refactoring, and feature development Infrastructure - Git - Docker - Cloud platforms (AWS / GCP / Azure) Preferred Qualifications - Experience building agent-based development workflows. - Familiarity with LLM APIs and AI tooling ecosystems. - Experience building developer tools or internal automation systems. - Startup or fast-paced product development experience. Who You Are - An engineer who uses AI as a core part of how you build software. - Strong problem-solver who can ship quickly while maintaining quality. - A builder who enjoys experimenting with new AI-powered development paradigms. The Hiring Journey - Screening with TA Partner (30 min) - Technical Interview with the VP of AI (45 min) - Culture Fit Interview with the Hiring and People Team Members (30 min) - Offer Ready to Join? If this role excites you, and you see yourself matching at least 85% of the criteria above, what are you waiting for? Let’s gooo! 🚀

The Opportunity QuoIntelligence turns millions of raw signals into finished Cyber Threat Intelligence (CTI) that security teams across Europe act on every day. The ML layer is what makes that possible: classification and enrichment today, AI-powered analysis through Agent Karla next. The ML team is small (2 people today), and the infrastructure is lean. You will own end-to-end production systems, from improving existing NLP pipelines, fine-tuning LLMs, building evaluation frameworks, and orchestrating AI agents in the cyber domain. What you ship, customers see. What You'll Do - Improve the production ML stack. The enrichment and classification pipelines work and serve real customers. They were built for speed, not longevity, so there's room to improve them. You'll ship at least one measurable improvement in your first 90 days. - Own model evaluation end-to-end. Quality metrics, ground truth labeling, offline/online evaluation: you'll design the framework the team uses to measure whether models are working in production. - Ship something the stack can't do today. The existing pipelines handle classification and enrichment. What comes next is open. You'll propose your first project in your first quarter, build it, and measure whether it works. - Expand agent capabilities. Help grow Agent Karla's intelligence by building new orchestration patterns and retrieval strategies using open-source frameworks like LangGraph. - Work directly with the IntelOps team. Your models serve the intelligence operations team; you'll validate performance against real-world threat scenarios, not benchmarks. AI-First in Engineering AI fluency is a company-wide standard at QI, not a department initiative. For engineering, three principles define the bar: - You build with AI-assisted tools daily (Cursor, Claude, whatever makes you faster). But you also know when AI-generated code introduces risk. You can evaluate whether an AI suggestion is reliable in a security-critical codebase, and you understand the difference between shipping fast and shipping recklessly. At a cybersecurity company, that judgment matters more than speed. - You evaluate new AI tools critically, adopt what works, and drop what doesn't. You have opinions on which tools are good and why, grounded in your own usage, not in what you read on LinkedIn. - Every model and pipeline has a clear definition of success before it ships. AI accelerates the iteration loop. Without clear success criteria, that speed is wasted. What You'll Bring Must-haves: - Production ML deployment. You've taken models from notebooks to production and maintained them over time, as part of systems that serve real users. - NLP and LLM grounding. Text classification, NER, summarization, embeddings, transformer-based models. You understand the fundamentals well enough to choose the right approach for a given problem, not just the newest one. - Comfort with messy data. Unstructured text with noisy, inconsistent signals. If your ML experience is limited to clean benchmark datasets, this role will frustrate you. - Python: It's the team's language. - Open-source mindset. You've worked with Hugging Face, spaCy, OpenNMT, or similar. If your entire career has been inside proprietary ecosystems with no exposure to open-source equivalents, that's a blocker. - AI fluency. Active daily use of AI-assisted development tools. You can show something you built or completed using AI, not just tell us you're interested. Nice-to-haves: - Experience with agent frameworks (LangGraph, LangChain, or similar) and orchestration patterns (ReAct, tool-calling, multi-agent systems) - Fine-tuning experience with open-source models (Qwen, LLaMA, Mistral) - Experience with inference servers and popular backends (e.g. NVIDIA Triton, vLLM, etc) - Familiarity with data orchestration tools (Kestra, Airflow, Prefect) - Cybersecurity or threat intelligence domain knowledge (genuinely not required; curious ML engineers ramp fast) What We Offer A small team where your work hits customers. No layers between your model and the intelligence product that clients rely on. Ownership, not just tickets. Your team lead defines priorities; you own how to solve them. You'll review the full service stack and model portfolio, then decide what to change and execute independently. Constraints that force creativity. We believe in using the simplest solution that gets the job done. We keep things lean and pick tools carefully. The interesting problems here come from making that work: squeezing more out of distilled models and designing pipelines smart enough to ship on the available hardware. Ethical red lines. QI holds the "Cybersecurity Made in Europe" label and serves as ENISA (the EU's cybersecurity agency) partner. We're upfront about what our AI can and can't do, and our compliance record backs it up. In a market full of AI hype, that credibility is a real advantage. Growth trajectory. Pre-Series A, revenue growing fast, Series A planning underway. You'd be shaping the ML direction for a company that still has ~40 people. The Process - Recruiter Screen - AI fluency screen - Take-home task: a real evaluation problem from the team's work. You can use AI tools, but you'll defend your reasoning and choices in the next round. - Hiring manager interview: walk through the task, then broader technical and behavioral assessment. A team member may join. - CEO/CTO Interview - Offer and Background Check We welcome applications regardless of gender, nationality, ethnic origin, religion, disability, age, or sexual identity. Diversity is key to producing high-quality intelligence.

Role Description Wir suchen einen Senior Shopware Entwickler mit Shopware 6 Erfahrung & AI Affinität, der transparente Kommunikation, sauberes Coding und qualitativ hochwertige Arbeit nicht nur als Floskel verwendet. - Konfiguration und Weiterentwicklung komplexer Shopware Projekte - Ein Mix aus Backend und Frontend – JavaScript (Vue.Js, React), PHP (Symfony), Git - Technische Verantwortung in Projekten und enge Zusammenarbeit mit Projektmanagern - Entwicklung von innovativen Ideen für den E-Commerce - Erkunden und Lernen von modernen und neuen Webtechnologien Qualifications - mind. 2-5 Jahre praktische Erfahrung im Full-Stack-Development - Mind. 1 Jahr Erfahrung in Shopware 6 - mind. Niveau B2 in der deutschen Sprache - Fundierte Kenntnisse in PHP (Symfony) - Erfahrung mit JavaScript-Frameworks wie Vue.js oder React - Routine im Umgang mit Git - AI-Affinität - auf dem neusten Stand mit AI Tools & Lust mehr dazuzulernen - Verantwortungs- und Qualitätsbewusstsein - Lösungsorientierte und selbstständige Arbeitsweise - Ausbildung zum Fachinformatiker/Anwendungsentwickler oder Bachelor Studium in Bereichen wie Mathematik & Computer Science Benefits - 100 % remote - Hands-on, strukturiert und lösungsorientiert – mit Fokus auf technischer Automatisierung und Effizienz - Kein Prozesschaos - Unsere Dokumentation und Abläufe sind digital und effizient - Individuelle Ziel- & Gehaltssysteme - Von BahnCard bis WalkingPad - Koro, Zalando, FC Moto, Wöhrl – 80–90 aktive Kunden, unter anderem aus Werbung auf ProSieben oder Höhle der Löwen - Du entscheidest - minimaler bis starker Kundenkontakt - Weiterbildungsmaßnahmen gehören für uns zum Alltag - Abwechslungsreiche Aufgaben und Projekte - Wir leben Feedback und fördern dies! - Teamspirit wird bei uns großgeschrieben – inklusive Nerfgun und lustige Memes Company Description Shopware Agentur 8mylez - Deine Experten aus Paderborn, Deutschland. Begleite uns auf dem Weg zur besten Shopware Agentur!

• Research, design, and develop innovative generative AI models and algorithms. • Implement and optimize deep learning architectures for generative tasks, focused on text generation. • Collaborate with cross-functional teams to define goals, requirements, and deliverables and implement the features. • Train and fine-tune models using large-scale datasets and advanced techniques. • Evaluate and assess model performance, making necessary adjustments to improve results. • Stay up-to-date with the latest advancements in generative AI trends and tools and contribute to the team's knowledge base. • Write clean, efficient, and maintainable code, following best practices and coding standards. • Document research findings, methodologies, and technical specifications. • Participate in code reviews and provide constructive feedback to peers. • Contribute to the development of tools and frameworks to facilitate generative AI research and development. • Interpret insights from our pre-sales, sales, customer success teams and clients to create AI-based features.