Top Artificial Intelligence Jobs in Germany

Design, build, and maintain an internal AI provisioning platform for LLM access, observability, governance, cost management, and secure integrations. Collaborate with AI engineers to support cloud-native, agentic workflows and monitor reliability and performance of production AI systems.

Lead strategy and roadmap for an internal AI platform, coordinate cross-functional teams to implement AI use cases, ensure AI compliance and governance, support GTM for AI features, and manage agile delivery while leveraging data and LLM-driven insights to evolve the platform.

Design, build, and maintain production-grade AI/LLM backend services and APIs. Integrate third-party LLM APIs, develop cloud-native architectures on AWS, extend Terraform infra and CI/CD, implement testing/monitoring, and build agentic orchestration workflows while collaborating with cross-functional teams.

Lead end-to-end partner digital journey and define strategy for a central AI platform. Drive LLM orchestration, agentic workflows, RAG patterns, API integrations, partner design system ownership, cross-functional alignment, and mentor product colleagues.

As an AI Product Manager, you will lead the application of AI in Health Information Systems, manage product portfolios, and ensure compliance with healthcare standards while delivering technology solutions that enhance patient care.

Lead MLOps Engineer responsible for building and automating the lifecycle of multimodal agentic AI: orchestration (LangChain/LangGraph), training pipelines for LLMs and CV, containerized model deployment, low-latency inference, model monitoring/drift detection, and optimizing ML infrastructure for performance and cost.

Serve as a forward-deployed technical partner translating customer workflows into scalable AI deployments. Drive adoption, own end-to-end deployments and integrations, build prototypes and demos, troubleshoot solutions, design integration architecture, and feed customer insights to product and engineering teams to scale enterprise adoption.

Lead and grow a consulting practice delivering software engineering and AI/ML projects: manage client engagements end-to-end, identify high-value use cases, drive sales and marketing, recruit and mentor a professional services team, and shape company strategy and brand with a path to equity and executive leadership.

Work directly with founders to define and execute a SaaS strategy for getML: product roadmap, IP/open-source strategy, financial modeling, growth paths (organic or VC), partnerships, fundraising, and operational planning toward a leadership role.

Lead international commercial expansion and scale sales teams as a manager-of-managers. Own market openings, replicate GTM playbook across geographies, design SDR/AE structures, manage CRM and Sales AI tooling, run pipeline reviews, coach field sales, close key deals, and own country-level P&L while growing the company from ~50 to 100+ employees.

Join as the founding engineer to own architecture and ship features for an AI-driven adaptive learning platform. Build full-stack (Next.js/TypeScript/Python), implement LLM agents and RAG pipelines, iterate with schools, and shape technical strategy and hiring as an equity-holding co-founder.

Define and deliver the Workplace AI & Automation roadmap using M365 Copilot, Copilot Studio, and Power Platform. Design and integrate end-to-end automations and copilots, ensure security/compliance and responsible AI, operate ALM pipelines, drive user adoption, manage licensing and costs, and align solutions with business goals.

Define and evolve the Workplace AI & Automation roadmap; design and build end-to-end M365 Copilot and Power Platform solutions; integrate systems via APIs and Microsoft Graph; ensure security, compliance, ALM and lifecycle management; drive user adoption and manage licensing and portfolio costs.

Build, deploy, and maintain production GenAI/LLM/NLP capabilities for in-car infotainment. Design scalable AI systems, agentic workflows, evaluation pipelines, and cloud ML infrastructure. Improve reliability, safety, and quality via testing and guardrails, and integrate AI features into Android Automotive OS and Linux environments.

Build and own Python backend systems that orchestrate multi-provider AI model pipelines for generating music videos. Improve reliability, latency, and cost, instrument production systems, contribute to prompt engineering, model evaluation, and fine-tuning, and collaborate on architecture and new product surfaces.

Participate in a 14-day notification-sorting pilot using a qualifying Google Pixel device. Interact naturally with notification-generating apps, complete daily sorting tasks and short feedback/check-in forms, report issues, and follow project instructions. Work is remote, short daily sessions (10–15 minutes), managed via Upwork.

Design and build backend services, cloud-native infrastructure, data pipelines and AI deployment workflows for an edge-to-cloud industrial AI platform. Work with founders, ML/CV engineers and pilot factories to deploy, monitor and scale production-grade systems, dashboards, and sustainability KPIs.

Develop scalable cloud infrastructure and robust software solutions for AI/ML, collaborating with engineers and scientists to enhance scientific research.

Post-sales technical lead for strategic EMEA accounts, advising on cloud-native and AI deployments. Own technical relationships, troubleshoot escalations, design architectures for LLM/agentic workloads, drive product adoption, deliver training, and feed customer insights to Product and Engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Build end-to-end AI-native product features across frontend, backend, and AI integrations. Design and deploy reliable agent workflows, integrate LLMs/memory/tools, optimize streaming low-latency interactions, improve observability, fallback and recovery mechanisms, and iterate on production failures to increase reliability of multi-step workflows.

Own end-to-end development of AI-powered product features: design prompts, tools, memory and agent workflows; deploy and stabilize LLMs in production; optimize latency, cost, and reliability; build evaluation frameworks; debug model, orchestration, infra, and UX issues; and collaborate with product and engineering to deliver measurable improvements.

Design, build, and deploy production AI agents and backend services. Integrate with ERPs and external APIs, handle structured and unstructured data, implement memory/retrieval and orchestration pipelines, and maintain cloud infrastructure, observability, CI/CD, and robustness for scalable agent workloads.

Own, design, and develop the AI Data Platform: maintain a high-scale data product platform (≈400K pipeline jobs/month, petabyte scale), architect cross-cloud data infrastructure, build and operate automated data pipelines, ensure data observability and quality, and implement AI-driven features and AI data agents to expose AI-ready data products.

Develop backend features and integrations for Nextcloud, including authentication, storage, audio/video chat, and AI services. Design databases and systems, integrate LLMs and speech/vision models, ensure quality, and work on time-boxed projects within an open source, distributed team.