Top Artificial Intelligence Jobs in Germany

Identify and contact manufacturing prospects across Europe, engage operations, production, and quality leaders, qualify leads, and book meetings for Account Executives. Partner with AEs on pipeline, messaging, and market feedback while helping develop the company's go-to-market playbook. The role requires B2B sales experience, strong communication, organization, initiative, and interest in AI, robotics, or manufacturing technology.

Seeking a Senior Software Engineer to build AI inference systems. Responsibilities include optimizing GPU kernels, architecting inference stacks, and defining benchmarking methodologies. Requires strong programming skills and experience in ML systems.

Build and ship governed AI applications for departments across the organization using LLM APIs, MCP servers, and internal AI platforms. Partner with business teams, lead technical enablement sessions, advise on AI integrations, document reusable patterns, and contribute to AI infrastructure including gateways, RAG, model operations, and evaluation frameworks. Ensure implementations meet security and governance standards while supporting use-case qualification and adoption reporting.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Manage relationships with AI-native startups, scaleups, and labs in Germany. Understand customer goals, develop AI solution strategies, coordinate NVIDIA technical and business resources, drive adoption of AI platforms, build ecosystem partnerships, develop customer references, identify emerging use cases, and represent NVIDIA at industry events. The role requires deep AI industry knowledge, strategic planning, cross-functional collaboration, and familiarity with NVIDIA’s software, server, and cloud stack.

Build and deploy AI agents and LLM-based systems that automate accounting, payroll, and tax workflows. Integrate AI with APIs and existing systems, while optimizing performance, cost, reliability, and quality. The role requires strong software engineering and Python skills, hands-on experience with LLM APIs, prompt engineering, structured outputs, and agent frameworks. You will work autonomously across multiple projects in a hybrid Berlin startup environment.

Embeds with client teams to deploy, scale, and operationalize enterprise AI platforms in production. Owns business outcomes including time-to-value, reliability, adoption, and scalability. Designs secure AI architectures, prototypes and productionizes solutions, translates technical strategies into measurable business value, leads client workshops, and creates reusable deployment patterns. Builds trust with senior stakeholders and codifies delivery learnings to scale the forward-deployed engineering practice.

Develop technical partnerships with global and regional system integrators and cloud providers. Design joint generative AI solutions, reference architectures, and scalable cloud integrations; support strategic customer deals; troubleshoot complex technical issues; enable partner AI practices through presentations, workshops, hackathons, and developer events; and provide product feedback to influence Anthropic’s roadmap and partner strategy.

Design, deploy, and maintain large-scale HPC/AI clusters including monitoring, storage, networking, virtualization, job schedulers, CI/CD, and automation tooling. Troubleshoot from bare metal through OS and application stacks, document standard methodologies, and support R&D and POCs for optimized AI and GPU computing workflows.

Build AI-powered features, LLM integrations, and multi-step agent workflows for requirements and systems engineering software. Integrate agents with internal data sources, reason over requirements documents and system specifications, address security and data privacy, and deploy AI capabilities into production. The role requires software engineering experience, hands-on LLM integration, end-to-end ownership, and collaboration in a startup environment.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Designs and delivers secure, scalable AI solutions using Google Cloud services, LLMs, agents, and automation. Translates business needs into technical architectures, prototypes AI proofs of concept, integrates solutions with enterprise applications, evaluates competing technologies, and establishes AI/ML standards and best practices. The role also advises stakeholders, coaches development teams, and promotes effective use of AI across the organization.

Drive Intel’s XeSS and AI-based graphics technologies from research through productization. Responsibilities include designing and training models, developing datasets, evaluating visual quality, optimizing performance, and integrating products across software and hardware. The role leads technical initiatives involving super resolution, frame generation, neural rendering, and future AI graphics workloads while collaborating with research, architecture, validation, and ecosystem teams.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

Leads SIXT’s AI-powered HR transformation, owning the strategy, roadmap, and rollout of a fully AI-built HRIS. Oversees AI HR architects and vibecoders, translates C-level priorities into execution, drives adoption of large language models and AI-first methods, manages cross-functional stakeholders, and ensures delivery quality, budget, and timelines. The role also evolves HR technology strategy through monitoring emerging AI capabilities and applying deep HR process, people data, and workforce management expertise.

Build and deploy production AI agents for fund-operations workflows. Own agent architecture, orchestration, RAG, evaluations, observability, deployment, provider fallback, cost, latency, and reliability. Develop shared AI platform patterns, document extraction, regression testing, human review controls, and customer-facing MCP capabilities. Partner with engineering teams, establish standards, and mentor developers while ensuring authorization, EU data residency, and production-grade operations.

Build and ship agentic AI systems for reward design and other data-team problems. Develop agents, LLM pipelines, RAG systems, retrieval and evaluation workflows, and determine where AI can deliver business impact. Work closely with data scientists, the CTO, and leadership, using Python and large behavioral datasets across cloud infrastructure. Establish the company’s AI engineering patterns, tooling, and standards.

Deploys, operates, and maintains AI and analytics applications in secure, on-premise and air-gapped environments. Responsibilities include building CI/CD pipelines, managing Kubernetes and OpenShift platforms, monitoring GPU-intensive workloads, supporting local generative AI services, and ensuring compliance with BSI IT-Grundschutz and German security requirements. The role also involves documentation, incident response, disaster recovery, technical evaluations, infrastructure improvements, and collaboration with product, development, security, and audit teams.

Leads and grows a team of Robotic AI Application Engineers delivering customer deployments. Owns feasibility, planning, integration, acceptance, escalation management, standards, resource allocation, and project prioritization across engagements. Partners with Product Management and R&D to incorporate field feedback into product development. Represents the applications team with customers, manages senior stakeholder relationships, and mentors engineers. Requires hands-on robotic or AI system deployment experience, technical leadership, complex project delivery expertise, and willingness to travel.

Deliver live workshops and applied labs on AI-assisted visual, video, document, and interactive content production. Demonstrate generative AI workflows, guide learners through brand-aligned assets, automate recurring tasks, and support vibe-coded interactive projects. Provide structured feedback, collaborate on course materials, and continuously improve content based on learner feedback and evolving AI tools.

Supports Account Managers and client teams by coordinating delegated tasks, tracking deadlines and follow-ups, preparing updates, managing workflows, communicating in German and English, reviewing AI-generated work, and improving repeatable account processes. The role requires strong client judgment, attention to detail, prioritization, technical adaptability, and hands-on use of AI tools and agents in a remote, async environment.

Build Python-based backend services, APIs, and integrations that embed AI capabilities into a SaaS platform. Develop scalable customer-facing features, connect AI outputs to workflows and dashboards, collaborate with product, design, and platform teams, and implement observability, monitoring, and error handling. Contribute to the AI integration stack, including SDKs, MCP servers, and LLM orchestration.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.