Top Artificial Intelligence Jobs in Germany

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Design and architect the Orion Generative AI Platform for scalable product deployment. Develop standardized technology stacks, reusable components, and best practices that accelerate GenAI delivery while abstracting technical complexity for application teams. Collaborate cross-functionally to integrate GenAI solutions and optimize platform performance using advanced software, data, and machine learning engineering practices.

Build and own scalable machine learning and generative AI systems for customer-facing and operational healthcare use cases. Develop production pipelines for training, evaluation, deployment, monitoring, and continuous improvement. Collaborate with product, engineering, data, and business teams; explore emerging AI technologies; define technical roadmaps; and uphold governance, model quality, versioning, observability, and compliance practices.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Own Soda’s technical content function across its website, blogs, webinars, videos, and audience growth. Build automated, agentic LLM content pipelines with structured outputs and evaluations while maintaining strong editorial judgment, distinct voices, and high-quality technical writing. Develop independently using Python or TypeScript, APIs, webhooks, and databases. Translate data quality and infrastructure concepts for technical audiences, collaborate with founders and customer engineers, and continuously improve workflows based on content performance.

Lead the architecture and deployment of enterprise agentic AI platforms using LangGraph, LLMs, RAG, enterprise integrations, cloud services, and secure governance patterns. Design scalable multi-agent workflows, state management, observability, evaluation, and human-in-the-loop controls. Partner with senior stakeholders, guide engineering teams, establish reusable architecture standards, and ensure production AI systems meet security, reliability, compliance, and operational requirements.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Own end-to-end product execution for an AI-native B2B enterprise MedTech software platform. Lead and develop 2–3 Product Managers while remaining hands-on in customer discovery, requirements definition, prioritization, engineering refinement, and product delivery across multiple squads. Partner with customers, Engineering, and Design; make product decisions amid ambiguity; improve product operating processes; and help shape AI-first product development.

Build and deploy deep-learning computer vision systems for autonomous spacecraft proximity operations. Responsibilities include 6-DoF pose estimation, synthetic-to-real training, model optimization and quantization, embedded and neuromorphic deployment, uncertainty and explainability, hardware-in-the-loop validation, self-supervised adaptation, and efficient cloud/HPC training. The role also supports camera selection, requirements definition, testing, and safety-critical space applications.

Owns commercial success, revenue, and strategic relationships for embedded HR technology partners. Responsibilities include managing a $10M+ annual bookings target, developing partner sales motions, enabling partner sellers, driving pipeline and ARR growth, resolving commercial issues, leading executive business reviews, expanding partnerships, coordinating cross-functional teams, and managing partner account managers and embedded sales leads.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

Owns end-to-end software-defined vehicle use-case roadmaps for automotive market segments. Conducts customer discovery with OEMs and Tier 1 partners, translates needs into product requirements, coordinates with platform capability teams, and defines success metrics. Supports go-to-market activities through customer narratives, demonstrations, and commercial packaging. Requires product management experience, automotive software knowledge, strong customer research and communication skills, and the ability to influence cross-functional engineering teams.

Own Roland Berger’s enterprise AI orchestration platform and its full lifecycle, including use-case intake, architecture, deployment, monitoring, and improvement. Lead AI projects, platform roadmap, budgets, vendors, adoption, governance, and compliance. Define responsible AI standards for agents, models, data protection, and access control while providing technical oversight and coordinating cross-functional teams. This is an internal leadership role focused on scalable, secure integration of AI capabilities into business applications rather than hands-on development.

Design and implement scalable generative AI and machine learning solutions for client and internal applications. Develop multimodal agents, automate workflows, and build deployment infrastructure using cloud platforms, Docker, Databricks, and LangGraph. Write maintainable code, document processes, present findings to stakeholders and clients, and collaborate on real-world business challenges while keeping current with data science and GenAI advancements.

Join Celonis' Orbit graduate program as an AI Solution Consultant to prototype AI+Process Intelligence solutions, run PoVs and hackathons, present business impact to executives, ensure adoption and value realization, and build reusable best-practice assets while specializing in domains like supply chain.

Defines AI strategy and transformation opportunities for enterprise accounts. Partners with executives, engineering leaders, deployment strategists, and operational stakeholders to identify high-value problems, shape commercial offers, establish scope and success criteria, manage risks, and align commitments with technical and operational realities. Drives executive alignment, decision-making, account expansion, and long-term engagement direction in ambiguous, high-stakes environments.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Build production-grade full-stack applications using React, Next.js, TypeScript, and Node.js. Design AI-native user experiences, conversational interfaces, and agent-driven workflows while shaping scalable architectures, APIs, and data structures. Collaborate with product, UX, and engineering teams, make technical decisions, and maintain high standards for testing, performance, and maintainability. The role requires product-oriented ownership from problem discovery through production delivery.

Lead the Sales Models portfolio (NBA, Churn, CLV), set roadmap and technical standards, manage a cross-functional Data Science and Engineering team, integrate models into Dynamics 365 and Power BI, translate model outputs into C-level decisions, and build governance and feedback loops from field sales to model iteration.

Advise clients and internal teams on mechanical and electrical infrastructure for AI and HPC data centers. Develop technical strategies, review designs and operational plans, assess resilience and capacity risks, and provide corrective guidance. Lead client presentations, technical workshops, and advisory sessions while translating emerging AI infrastructure trends into practical business guidance. The role is home-based in Europe with regular travel across Europe and wider EMEA.

Design and build practical AI solutions for ecommerce and business operations. Responsibilities include identifying high-value use cases, developing LLM applications, AI assistants, agents, RAG solutions, and automated workflows, and connecting them to enterprise data through Azure Databricks. The role also involves rapid prototyping, validating technical and business value, collaborating with data and platform teams, and supporting stakeholder adoption and change management.

Designs production-ready AI architectures for customer experience and contact center use cases. Responsibilities include RAG and knowledge architecture, AI evaluation and observability, contextual and agentic workflows, enterprise integrations, prototypes, technical demonstrations, presales solutioning, executive engagement, field enablement, and product feedback. The role optimizes AI systems for scalability, latency, cost, reliability, governance, and business outcomes while influencing CIO and CTO stakeholders.

Lead and expand a strategic financial-institution account across the DACH region. Build C-suite relationships, identify business challenges, develop tailored enterprise solutions, structure and negotiate multi-million-dollar SaaS deals, and execute joint business plans to achieve growth and profitability targets. Act as the client advocate internally, influence product strategy, collaborate across sales and delivery teams, and serve as a trusted advisor on analytics, fraud, risk, lending, and decisioning technologies. Travel may reach 50%.

Lead development and execution of ESG products, ensure regulatory compliance, collaborate with UI/UX and engineering, drive data-informed roadmaps, and adapt product strategy to evolving ESG/GRC legislation.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.