Top Artificial Intelligence Jobs in Germany

Develop TYTAN’s growth strategy by evaluating markets, customer segments, acquisitions, investments, and strategic partnerships. Build financial models, business cases, competitive intelligence, and commercial agreements while advising leadership and coordinating across engineering, finance, and legal. The role requires defense or deep-tech exposure, German and English fluency, extensive international travel, and willingness to undergo security vetting.

Build and deploy LLM-powered systems for construction tendering workflows. Own AI features end to end, from models and agents through backend services, APIs, and TypeScript front-end integrations. Develop RAG, tool-use, reasoning, and evaluation systems; maintain production monitoring and alerting; and collaborate with Product, Engineering, and Data. The role requires strong Python, TypeScript, React, Node.js, backend engineering, and experience shipping AI features to production.

Develop and research post-training methods for coding-focused LLMs, design and run experiments, build prototypes into products, collaborate with researchers, engineers, and MLOps, and communicate technical results to technical and non-technical audiences.

Designs and operates CI/CD pipelines, infrastructure automation, release workflows, and deployment platforms for robotics, embedded software, AI/ML, and edge systems. Builds cloud-edge infrastructure using containers and orchestration, improves software quality through automated testing and analysis, and supports deployment to Linux devices, simulations, and hardware-in-the-loop environments. Partners with software, robotics, embedded, AI/ML, and validation teams to improve reliability, observability, security, and engineering productivity.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Manages HARMAN’s enterprise AI services portfolio, including ChatGPT Enterprise, Microsoft Copilot, and GitHub Copilot. Owns service lifecycles, roadmaps, adoption, feature rollouts, vendor coordination, license governance, operational performance, and executive reporting. Partners with business, technology, procurement, legal, security, learning, and communications teams to ensure governed, effective adoption and continuous optimization of AI capabilities.

Build and operate reliable full-stack DevTools and AI reliability software using Vue.js, TypeScript, Node.js, Golang, PostgreSQL, ClickHouse, and AWS. Use AI coding agents throughout development, engage directly with developer and SRE customers, identify and solve reliability problems, own features from discovery through production, and participate in support and on-call rotations within a remote async-first team.

Grow Qure’s user base from scratch by testing acquisition channels, managing budgets, creating marketing assets, building measurable funnels, and partnering with product on activation-focused improvements. The role requires rapid experimentation across paid, organic, community, video, and content channels, using data to optimize acquisition economics and eliminate underperforming efforts.

Own the enterprise AI platform operating model, standards, governance, security, deployment patterns, and transition from experimentation to production. Define reusable LLM, RAG, evaluation, gateway, and application-integration patterns; guide MLOps engineers and external partners; establish cost and usage visibility; and coordinate architecture, cloud, security, and data teams. Report roadmap, risks, adoption, and production readiness to senior and CIO-level stakeholders.

The role focuses on providing on-the-job training alongside classroom education in Data Science and Artificial Intelligence over three years, culminating in a degree. It includes an internship, language and IT training, and development of personal and leadership skills.

Lead workshops, architect and design Workday Extend applications, deliver requirements and design to developers, manage and guide Extend consultants, liaise with clients and Workday, and support pre-sales and AI-enabled solutioning.

Drive net-new enterprise sales for IFS Loops, an agentic AI platform for industrial markets. Hunt strategic accounts, generate demand, lead consultative discussions with senior technical and business stakeholders, qualify and close large ACV opportunities, and position autonomous workflows and decision intelligence. Partner with regional sales teams, product specialists, and solution engineering to execute go-to-market initiatives and expand adoption of AI-driven automation.

Work directly with the founder and CEO across customer discovery, market research, outbound outreach, customer conversations, positioning, CRM and process setup, and materials for customers, investors, and partners. The role requires creating structure in an early-stage AI venture, learning technical products quickly, and taking ownership of broad commercial and operational tasks.

Serve as a technology partner to HR, analyzing hire-to-retire processes and translating business needs into HRIS, automation, and AI solutions. Build and deploy Claude-based applications, n8n and UiPath workflows, Python automation, integrations, and secure AI tools. Lead HR technology initiatives through requirements, design, testing, implementation, and continuous improvement while managing access governance, sensitive data protection, service desks, and digital employee experiences.

Conduct foundational AI safety research with Yoshua Bengio and the LawZero team. Develop theoretical frameworks, safe AI architectures, and probabilistic safety guarantees under finite data and compute constraints. Analyze existing research, formalize safety requirements, guide empirical experiments, publish scientific work, and communicate complex ideas to technical and non-technical audiences. The role focuses on building Scientist AI systems that are capable, transparent, and safe by design.

Build production-grade evaluation, observability, and optimization infrastructure for multimodal LLM agents. Develop online and offline evaluation pipelines, golden datasets, quality gates, trace analysis, replay and retention systems, and monitoring workflows. Analyze agent behavior using analytical databases to identify quality, latency, reliability, and cost issues. Collaborate with backend engineers to improve retrieval and reasoning agents while making architectural decisions for reliable AI systems.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Designs end-to-end AI transformations for complex enterprise customers. Advises HR, Finance, and IT executives; leads requirements discovery, process design, configuration guidance, data readiness, governance, and adoption workstreams. Partners with Product, Engineering, and technical teams to align AI agents, data models, security, and integrations. The role requires enterprise consulting experience, strong business acumen, AI solution design expertise, executive communication, and travel of up to 50%.

Lead hands-on development of AI-powered workflows, coding-agent integrations, and C++ development capabilities for CLion. Design agent architectures, tool integrations, benchmarks, and evaluation frameworks while prototyping debugging, code understanding, refactoring, and navigation workflows. Collaborate with JetBrains AI, ML, product, QA, and C++ teams to shape technical direction, roadmap priorities, and AI strategy for developer tools.

Supports AI and data consulting projects through market sizing, segmentation, pricing, optimization, predictive modeling, KPI development, dashboards, workshops, and executive presentations. The intern analyzes large datasets, develops practical client solutions, communicates complex findings to nontechnical audiences, and contributes to cross-functional strategy teams.

Lead the architecture and delivery of end-to-end AI solutions for complex global Workday customers. Design data flows, integrations, configurations, security controls, and AI experiences across Workday Agents, the Agent Platform, Sana, external LLMs, and third-party systems. Advise senior stakeholders, lead technical workstreams, oversee AI deployments, manage risks, and partner with Product and Engineering to improve scalable AI tooling and deployment patterns.

Lead EMEA customers in deploying and optimizing large-scale AI inference on multi-node GPU clusters. Architect dense and Mixture-of-Experts inference pipelines, improve quantization, speculative decoding, prefill/decode disaggregation, KV cache management, and workload scheduling. Collaborate with NVIDIA product teams and engage the developer community through workshops, hackathons, and reference architectures. Advise ML engineers, researchers, and systems architects on high-performance inference across thousands of GPUs.

Develops and operates production-ready AI and machine learning solutions, including end-to-end ML pipelines, AI APIs, LLM applications, model deployment, monitoring, and experiment tracking. The role applies MLOps and DevOps practices on Microsoft Azure, supports Data Scientists and Engineers, establishes AI architecture standards, evaluates emerging technologies, and helps deliver secure, ethical, privacy-compliant solutions.

Serve as the bridge between AI and automation strategy, business needs, and platform delivery. Identify and prioritize use cases using ROI and data-driven assessments, translate needs into requirements and user stories, support roadmap and backlog planning, define KPIs and value measurement, and promote responsible AI adoption through rollout, training, and communication.

Design and maintain an internal AI provisioning platform supporting LLM and MCP Server access. Build observability, logging, alerting, governance, security, privacy, rate limiting, and cost-management capabilities for production AI systems. Collaborate with AI software engineers on cloud-native and agentic workflows while maintaining reliable infrastructure. The role requires cloud infrastructure, Infrastructure as Code, programming, CI/CD, containerization, and familiarity with LLM gateways and telemetry platforms.