Top Artificial Intelligence Jobs in Germany

The role involves integrating AI tools into .NET and C++ applications, optimizing models for performance, and collaborating with engineers on deployment and monitoring.

Work as an intern building software and AI application features for production manufacturing systems. Collaborate with senior engineers to troubleshoot, document, and ship solutions, integrate the platform into customer environments, and translate customer requirements into technical implementations while learning production processes, IT infrastructure, and AI product deployment.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Join the AI Engineering team to design, build, deploy, and scale production AI systems. Translate business problems into AI solutions, industrialize ML models, design AI pipelines and platforms, ensure performance and monitoring, and drive MLOps best practices across architecture and governance.

Develop and optimize real-time, high-throughput signal processing on embedded GPU modules: implement parallel GPU kernels (FFT, filtering, detection), design zero-copy DMA ingest paths, build deterministic streaming pipelines, evaluate vendor SDKs, maintain board support and OS images, and validate performance with profiling, numerical/bit-true tests and CI integration. Collaborate in an interdisciplinary prototyping team and support integration/measurement campaigns.

Develop application/control layer for sensor demonstrators: design interfaces, implement mode/resource/config management, ensure operability, observability and testability (scheduling, health monitoring, data recording, operator API). Participate in prototyping, automated testing and CI/CD, and integration/measurement campaigns.

Lead Data & AI consulting engagements from strategy to delivery: identify high-impact use cases, define roadmaps, guide bespoke AI and platform development, manage stakeholders and project delivery, support sales and proposals, and create reusable assets while representing the discipline externally.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Lead the product lifecycle for B2B agentic AI systems (chat/voice bots and virtual sales assistants), integrate solutions with Salesforce CRM, collaborate across Sales, Marketing, Data Science, Product and Engineering, own conversational AI roadmap and KPIs, structure backlog, analyze performance, and research market trends and tooling to drive AI-driven corporate sales adoption.

Drive AI adoption by identifying and prioritizing AI use cases, developing business cases, collaborating with data and tech teams to implement solutions, facilitating workshops, enabling stakeholder adoption, and ensuring responsible AI governance and value realization across the enterprise.

Lead SEO, Generative Engine Optimisation (GEO), and HubSpot website management to drive organic pipeline. Own technical, on-page, off-page SEO, AI search presence, CRO experiments, multilingual site governance, agency coordination, and reporting that ties organic activity to revenue.

Lead enmacc's transition to an AI-first trading platform by developing AI solutions and predictive models tailored for energy trading, overseeing a unified Data & AI team, and driving product innovation.

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

Design, build, and operate backend services for an AI-driven financial assistant. Own full SDLC, ensure performance, scale, resilience, and collaborate in small autonomous teams.

Design, build, evaluate, and integrate AI-powered services, APIs, agentic workflows, and automation into production systems. Develop backend services (Python, FastAPI/Flask/Django), evaluate and deploy models (LLMs, speech), integrate with internal/external services, support model evaluation/training/fine-tuning, and collaborate with platform and product teams to operationalize AI solutions.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Build, prototype, and productionize applied AI workflows for construction projects: manage experimentation pipelines, error analysis, evaluation/scoring, dataset curation, and improve ML/data pipelines and monitoring for complex unstructured data sources.

Design rigorous, deterministic advanced physics problems and provide complete verified solutions for AI training and evaluation. Create reproducible simulations or computational workflows (using Python and scientific libraries), document reasoning clearly, and ensure technical precision. Contribute to problem design that tests deep multi-step physical reasoning.

Own the vision, roadmap, and backlog for the MLOps/LLMOps Platform and AI Service tooling. Prioritize features, run agile ceremonies, steer technical discussions, represent users, manage dependencies and risks, and align international stakeholders to enable data scientists and ML engineers to build GenAI applications.

Lead integration of AI capabilities into the osapiens HUB by gathering and prioritizing application-team requirements, translating business needs into AI solution scenarios, monitoring AI market trends, measuring AI component usage/performance, and building cost structures and business cases to prioritize AI investments.

Own acquisition, onboarding, and activation for JetBrains AI. Improve product entry points, trials, demos, and onboarding flows. Lead experiments, analyze funnels and cohorts, remove friction, and partner with design, engineering, analytics, marketing, and monetization to drive activation and conversion.

Design and implement agentic tools, translating product needs to production, scale prototypes, improve semantic search, build evaluation pipelines.

Design and develop Generative AI applications, building scalable full-stack solutions, managing various libraries, and collaborating across teams to enhance AI user experiences.

Drive market growth of IFS Loops, engaging industrial accounts to sell AI-driven workflows. Lead consultative sales and close high-value opportunities.