• Own and evolve the Grove patient-facing mobile application • Absorb the existing codebase and establish architecture standards • Build for iOS and Android from a shared codebase • Collaborate with offshore development team • Manage onboarding and intake flows for patient registration and identity verification • Render patient records and clinical summaries from AWS HealthLake • Implement push notifications and offline-tolerant state management • Set code review and architecture standards with KMS Technology mobile team

Role Description - Du entwickelst und betreibst unsere souveräne LLM-Plattform inklusive OpenAI-kompatiblem Gateway, Modell-Serving und GPU-Infrastruktur. - Du implementierst kundenspezifische Wissensanbindungen (RAG) mit Vector-Datenbanken, Embeddings und Reranking. - Du stellst Kostenkontrolle, Mandantentrennung, Datenschutz und Guardrails sicher. - Du etablierst Monitoring und Observability für Performance, Qualität und Nutzung der KI-Dienste. - Du betreibst und entwickelst unsere containerbasierte Plattform auf Basis von Docker, Coolify und Kubernetes weiter. - Du automatisierst Deployments und verantwortest CI/CD-Prozesse für unsere Eigenentwicklungen. - Du betreust unsere Linux-, Cloud- und Datenbank-Infrastruktur und sorgst für Stabilität, Sicherheit und Verfügbarkeit. - Du baust Monitoring-, Logging- und Alerting-Lösungen auf und entwickelst diese weiter. - Du gestaltest die Architektur unserer Plattform- und KI-Services aktiv mit. - Du etablierst Infrastructure as Code und standardisierte Deployment-Prozesse. - Du sorgst für datenschutzkonforme und mandantensichere Betriebsmodelle. - Du dokumentierst Lösungen, teilst Wissen im Team und evaluierst neue Technologien im Bereich Platform Engineering und KI. Qualifications - Abgeschlossenes Studium der (Wirtschafts-) Informatik oder eine vergleichbare Qualifikation. - Mehrjährige Erfahrung im DevOps-, Plattform-Engineering- oder Cloud-/Infrastruktur-Umfeld. - Fundierte Praxiserfahrung im Betrieb containerbasierter Anwendungen und moderner Plattformen. - Erfahrung im Aufbau und produktiven Betrieb von LLM- und KI-Diensten, idealerweise in selbst gehosteten Umgebungen. - Sehr gute Kenntnisse in Kubernetes, Docker und Linux. - Erfahrung mit CI/CD-Pipelines, Cloud-Plattformen (insbesondere Azure) sowie PostgreSQL. - Praktische Erfahrung mit LLM-Gateways, RAG-Architekturen, Vector-Datenbanken und Modell-Serving-Lösungen. - Solide Python-Kenntnisse für Automatisierung und Integrationen. - Eigenständige und strukturierte Arbeitsweise mit hoher Eigenverantwortung sowie Sicherheits- und Datenschutzbewusstsein. - Ausgeprägtes Architektur- und Platformverständnis. Benefits - Offene und kooperative Unternehmenskultur sowie eine vertrauensvolle Arbeitsatmosphäre. - Umfassende Einarbeitung mit Unterstützung des gesamten AIS Teams. - Flache Hierarchien – kurze Entscheidungswege und hohe Gestaltungsfreiheit. - Strategische Gestaltungsrolle mit großem Einfluss auf die zukünftige Plattformlandschaft. - Mitarbeit an einem der spannendsten Technologiefelder der IT: souveränes LLM-Hosting und KI-Infrastrukturen. - Moderne Infrastruktur mit Kubernetes, Docker, CI/CD und Cloud-Technologien. - Möglichkeit, Standards, Prozesse und Architekturentscheidungen aktiv mitzugestalten. - Individuelles Weiterbildungsbudget für Konferenzen, Zertifizierungen und Fachtrainings. - Klare Karriere- und Entwicklungspfade. - Möglichkeit zur Spezialisierung als AI Platform Engineer bzw. LLMOps-Experte. - Kleines, fokussiertes und agiles Team mit kurzen Entscheidungswegen. - Offene Feedback-Kultur mit regelmäßigen 1:1-Gesprächen und Retrospektiven.

Role Description Design, build, and operate a sovereign AI toolchain used in isolated or tightly controlled environments, including model serving, retrieval, orchestration, observability, and secure platform operations for enterprise AI engineering workloads. This role centers on open-source or open-weight LLM stacks, air-gapped or isolated deployment models, Kubernetes-based platform engineering, GPU-enabled environments, and auditable AI operations suitable for sovereignty-sensitive programs. Key responsibilities - Design and run Kubernetes environments optimized for AI inference, retrieval, experimentation, and agent execution in secure or isolated settings. - Deploy and operate open-source or open-weight model stacks, model gateways, vector databases, and supporting platform components. - Build reproducible platform automation using Infrastructure as Code and GitOps approaches for stable, auditable delivery. - Manage local registries, package mirrors, secrets, access controls, storage, networking, and observability in environments with limited or no public cloud dependency. - Optimize GPU, compute, and storage usage for reliable AI workloads while maintaining security and data sovereignty requirements. Examples of market tools, models, and platform components expected: - Inference and local serving stacks such as vLLM, Ollama, llama.cpp, or OpenAI-compatible self-hosted endpoints. - Open-source or open-weight models appropriate for sovereign deployment, for example coding-capable and general-purpose families hosted internally through approved serving layers. - Platform tooling such as Kubernetes, Helm, Terraform, Ansible, ArgoCD, private registries, Qdrant or similar vector stores, and Open WebUI or comparable internal interfaces. - Developer-facing integration options such as VS Code-compatible extensions, Continue-style local model connectors, or editor integrations pointed at internal APIs instead of external SaaS endpoints. - Hardware awareness covering GPU-backed nodes, CPU-only fallback options, storage performance, network isolation, and on-prem or dedicated infrastructure patterns. Qualifications - 5+ years in platform engineering, DevOps, SRE, or MLOps, with strong Kubernetes and Linux expertise. - Proven experience with AI infrastructure, model serving, private or on-prem deployments, and production operations for LLM-based workloads. - Strong hands-on skills in Python plus automation tooling such as Terraform, Ansible, Helm, and GitOps workflows. - Good understanding of networking, storage, access control, monitoring, and operational hardening in high-security environments. - Comfortable working in sovereignty-driven environments where auditability, isolation, and controlled data handling are mandatory. Additional Information - You will be working in the European Union to meet our customers' data security and privacy requirements. - Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation. Company Description As Hungary’s most attractive employer in 2025 (according to Randstad’s representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. We have hundreds of large customers, corporations in Germany and in other European countries. DT-ITS received the Best in Educational Cooperation award from HIPA in 2019, acknowledged as the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team.

Title: Lead Instructor: MLOps / AI Platform Engineering Location: United States Job Description: U.S. Remote, Business UnitGeneral Assembly DepartmentInstruction (Full Time) Since 2011, General Assembly has transformed tens of thousands of careers through pioneering, experiential education in today’s most in-demand skills. As featured in The Economist, Wired, and The New York Times, GA offers training in web development, data, design, business, and more, both online and at campuses around the world. Our global professional community boasts 60,000 full- and part-time alumni — and counting. In addition to fostering career growth for individuals, GA helps employers cultivate top tech talent and spur innovation by transforming their teams through strategic learning. More than 21,000 employees at elite companies worldwide have honed their digital fluency with our upskilling and reskilling initiatives. GA has also been recognized as one of Deloitte’s Technology Fast 500, and Fast Company has dubbed us leaders in World-Changing Ideas as well as the #1 Most Innovative Company in Education. GA is at the leading edge of creating practical solutions to one of the most pressing challenges of our time - the future of work. As recognized by The World Economic Forum, BCG, the OECD and more, these are big challenges to which only a few companies are offering real solutions. In this role, you'll be speaking every day to corporate leaders who rely on GA to help them apply these solutions to their workforce of the future. Job Title: Lead Instructor: MLOps / AI Platform Engineering Company: General Assembly Client: Confidential – Customer Success Reskilling Location: Remote (Must work West Coast / Pacific Time hours) Duration: 2 Weeks (Starting Mid-June) Commitment: Roughly 30 hours per week Compensation: $11,500 - $15,500 (Estimated lump sum payment for one 60-hour program) About the Engagement General Assembly is delivering a specialized reskilling program designed to transition Customer Success and Account Management professionals into MLOps and AI Platform Engineering roles. As the Lead Instructor, you will be the face of lessons. You aren't just checking the math; you are bridge-building. You will lead experienced customer-facing professionals through the complexities of taking AI systems from pilot to production, ensuring they leave the 2-week intensive with a functional understanding of MLOps governance and deployment. What You’ll Do - Lead Live Instruction: Deliver high-energy, synchronous remote lectures and "prompt-along" sessions covering ML pipelines, model deployment, and monitoring. - Simplify the Complex: Translate high-level MLOps concepts (CI/CD for ML, governance frameworks) into digestible insights for learners who are experienced professionals but not career engineers. - Facilitate Hands-on Labs: Guide students through self-paced exercises and live troubleshooting within the Azure AI Foundry and ML environments. - Mentor & Office Hours: Provide real-time feedback during dedicated lab hours, helping students navigate technical roadblocks in Python and Azure infrastructure. - Drive Learning Outcomes: Ensure students can successfully articulate and execute model lifecycle management strategies by the end of the cohort. What You Bring - The Experience: 7+ years in software or data engineering, with at least 3+ years specifically in MLOps or ML platform roles in a production environment. - The "Teacher" Gene: Proven experience in technical instruction, bootcamp delivery, or corporate training. You should be comfortable "reading the room" in a virtual setting. - Azure Fluency: Deep, hands-on expertise with Azure ML and AI Foundry. You should be able to navigate these platforms in your sleep. - Technical Foundation: Proficiency in Python, Data Engineering fundamentals, and applying DevOps/CI/CD principles specifically to ML workloads. - The Credentials: AZ-900, AI-900, and DP-100 are required; AI-102 is preferred. - The Pedigree: Experience as an AI Platform or Azure ML engineer at a major tech firm (Microsoft, Google, etc.) is a massive plus. Note on Schedule: This is a high-intensity, 2-week engagement. Candidates must be fully available for 30 hours per week during the mid-June window and be prepared to operate on Pacific Time (PT) schedules to align with the learner cohort. Unless otherwise noted, remote positions can be performed from the following approved General Assembly operating countries. United States of America (states of operation may vary), Canada (provinces of operation may vary), United Kingdom, Australia, and Singapore.