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1. Technical Leadership & Execution * Own the end-to-end delivery of software projects, ensuring reliability, quality, and timely execution. * Maintain high engineering standards and reliable releases while driving clean architecture, scalable design, and solutions that balance speed with long-term maintainability. * Maintain a strong hands-on presence in codebases, architecture discussions, and complex problem-solving. * Align engineering choices with project goals and business direction. 2. Team Development & Mentorship * Mentor and guide team members across levels and help shape a high-performance, collaborative culture. * Conduct code reviews, define coding standards, and establish engineering best practices. * Coach junior developers and support their technical growth. * Facilitate knowledge-sharing, documentation, and continuous improvement. 3. Cross-Functional Collaboration * Work closely with designers, product managers, AI engineers, and operations to deliver complete, user-centric features. * Translate product requirements into clear engineering plans, tasks, and technical specifications. * Work together with leadership to identify risks, create mitigation plans, and ensure development stays unblocked. 4. System Architecture & Scalability * Participate in shaping the technical direction of the platform—architecture, integrations, observability, and patterns. * Ensure maintainability, performance, and scalability across services and client-facing applications. * Coordinate with Infrastructure/DevOps teams for deployments, CI/CD, monitoring, and testing workflows.
Full technical ownership from day one: architecture, hands-on MVP development, and pragmatic decision-making in an early-stage B2B SaaS. Close collaboration on product decisions, strong focus on backend and business logic, with the opportunity to grow into building and leading the engineering team over time.
You will build and own the commercial engine of Kindex. This includes identifying and approaching the right partners, shaping and refining the partner offer, negotiating terms, and closing agreements with integrity. You will design and test revenue structures such as commissions, packages, pilots, and featured formats, and turn early deals into repeatable, disciplined processes. You will manage a clear pipeline from first contact to long-term partnership, ensuring momentum, follow-through, and decisions. You will work closely with the founder on positioning and sequencing, helping define what comes next and what should wait. The challenge is not speed, but building traction that holds: growing partnerships, revenue, and trust together, without compromising quality or the family ecosystem vision.
- Define and own the overall system architecture - Build and evolve ClaimOS as an AI-native platform - Make key decisions around stack, infrastructure, and tooling - Establish engineering standards, processes, and culture - Hire and mentor future engineers - Work closely with product, customers, and partners on real-world integrations
If you want to create an advanced multimodal AI architecture, employing computer vision that works in the cloud as well as air-gapped on-premise - this is your opportunity! We are looking for a technical visionary to lead the development of our authoring suite: strategically as well as hands-on. This means you will not only create the technical roadmap, thereby setting the code and technological foundation, but also select the future members of your team and write significant portions of the code yourself. Your challenges: Instead of creating the next standard SaaS CRUD application, you will architect a sophisticated system that solves unique challenges in high-performance computer vision and multimodal LLMs, not just in the cloud, but also fully on-premise and air-gapped.
Du bist verantwortlich für den "Motor" unseres Systems. Mission: Entwicklung einer Pipeline zur automatisierten Destillation von Large Language Models auf effiziente, lokal lauffähige Agenten. Innovation: Du löst das Dilemma zwischen Intelligenz und Privatsphäre. Challenge: Wie bringen wir GPT-4-Level Reasoning in eine lokale Node, ohne Daten in die Cloud zu senden? Du baust "Pocket-Sized Experts" für die Industrie.
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