Top Artificial Intelligence Jobs in Germany

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.

Lead development and integration of ML models and agentic architectures to fully automate supplier search. Build scalable AI agents, RAG/vector search, MLOps pipelines, optimize inference, monitor agents at scale, mentor juniors, and ensure high reliability and cost control.

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.

The Backend Engineer designs and develops backend services integrating AI/ML capabilities, focusing on scalability, performance, and API development, while collaborating with frontend teams and maintaining distributed systems.

Uncountable seeks a Platform Engineer focused on Generative AI to develop AI-powered tools for scientific R&D, requiring fullstack development skills and a passion for advancing user needs.

Lead and orchestrate AI-driven transformation across the Private Bank: industrialize use cases into scalable capabilities, set standards and governance for workflows, pricing, decisions and integrations, prioritize initiatives for client value, enable delivery units for end-to-end optimization, and foster an AI-enabled transformation culture.

Apply AI-driven and physics-based protein engineering methods (Rosetta/PyRosetta, generative models, PLMs, AlphaFold) to design and optimize proteins. Build Python analysis pipelines, analyze sequence/structure/experimental data, benchmark methods, collaborate with experimental teams in DBTL cycles, and develop internal tools and infrastructure.

Develop and support internal AI platform capabilities, drive adoption, manage governance processes, and serve as a senior technical authority for AI initiatives.

As a Senior AI Engineer, you will design and implement process automations, collaborate across teams, and ensure secure delivery of banking solutions using GenAI technology.

As a Senior Full Stack Engineer for the AI Platform, you will build solutions enabling rapid GenAI agent development and deployment, impacting healthcare and other sectors. Your role encompasses designing full-stack applications, applying secure SDLC practices, and collaborating on impactful AI systems.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior HPC and AI Network Software Architect will develop scalable AI systems focused on distributed training and inference, collaborating on software and hardware integration to enhance performance.

Lead product vision and roadmap for Agentic IDEs and CLIs, drive developer research and adoption, define success metrics, prioritize data integrations, coordinate GTM and upskilling, and align stakeholders to deliver scalable AI-driven developer workflows.

Assist the Group Product Manager with product initiatives, write structured documentation, maintain competitive analyses, and track initiative progress while collaborating with technical teams.

Design and integrate AI capabilities into SAP-based enterprise applications, build and scale prototypes into production using ABAP Cloud on SAP BTP, integrate LLMs with orchestration tools (LangChain/LangGraph), apply RAG approaches for enterprise solutions, and collaborate with customers on claims-management transformation.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design and deliver production-grade AI solutions across structured CRM and unstructured data. Drive technical strategy for agentic workflows, RAG/vector DB systems, and real-time reasoning. Partner with AI Platform on LLMOps and cloud infrastructure, act as a technical authority, and contribute as a high-impact individual contributor to ship customer-focused AI products.

Architect and implement scalable, robust systems for ML-driven scientific research. Build and optimize distributed data-processing and model-training pipelines on cloud or HPC with GPUs/tensor accelerators. Develop tools to train, optimize, and scale ML solutions, collaborate with scientists and engineers, document best practices, and maintain high code quality.

Work on production AI systems that automate tax workflows: document intelligence, LLM/RAG retrieval systems, ML/data pipelines, prediction and suggestion models, and monitoring in real-world settings.

Design, build, and operate production AI systems for tax advisory: document intelligence, RAG over tax/legal knowledge, data pipelines, prediction and anomaly detection, and monitoring/evaluation. Collaborate with engineers and domain experts to deploy reliable, explainable AI that reduces manual tax work.

Lead and coordinate data protection, compliance, and AI governance processes across the organization. Maintain and review governance documentation, handle data protection requests and approval workflows, advise product and engineering on compliant AI practices, and monitor regulatory developments. Act as liaison between operational teams, management, and governance stakeholders while improving governance procedures and supporting implementation of AI-driven public-sector solutions.

Design and implement testing strategies and automated frameworks for AI/ML systems; evaluate model performance, robustness, biases, hallucinations, and security; analyze datasets and outputs for quality and compliance; collaborate with engineers and product teams to improve AI solutions and document testing findings and best practices for trustworthy AI in public administration.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.