Top Artificial Intelligence Jobs in Cologne

Own AI Investing product areas across financial services, defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features quickly, monitor performance, and iterate using data, research, feedback, and production insights.

Own product areas across AI Finance, including insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use research, feedback, experiments, and performance data to iterate after launch.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, product direction, scope, metrics, roadmaps, and launches while collaborating with engineering, design, data, compliance, operations, and business teams. Simplify complex financial products, ship quickly, monitor performance, use feedback and experiments, and continuously improve customer experiences while balancing business impact, compliance, and operational complexity.

Provide technical support for ClickHouse and Langfuse customers across EMEA and global regions through cases, chat, Slack, community channels, and calls. Troubleshoot database, cloud infrastructure, AI application, tracing, and evaluation issues within SLA and on-call rotations. Develop documentation and educational content, assist with technical proofs of concept, mentor colleagues, escalate bugs, collaborate with Engineering and Product teams, and support customer implementations directly in codebases.

Develop and optimize GPU- and CPU-accelerated numerical software libraries for AI, analytics, image processing, computer vision, and scientific simulation workflows. Responsibilities include designing scalable algorithms, performance tuning, benchmarking across architectures, collaborating on feature requirements and technical roadmaps, providing technical leadership, and improving library usability and performance.

Designs end-to-end enterprise AI architectures on AWS, including LLM orchestration, integrations, cloud infrastructure, security, scalability, and governance. Leads technical discovery workshops, creates architecture documentation and standards, guides engineering squads, reviews designs, presents decisions to senior stakeholders, and evaluates third-party AI platforms for buy-versus-build decisions. Requires extensive senior architecture experience and fluent Dutch and English.

Own the technical presales strategy for complex enterprise deals across DACH and expansion markets. Lead discovery, technical deep dives, customized demonstrations, solution design, POCs, and POVs. Translate AI Agent and contact center technologies into business value for senior stakeholders, provide competitive analysis, and collaborate with product teams. Represent NiCE Cognigy through events, webinars, trade shows, and thought leadership while supporting internal enablement and customer feedback.

Lead identification, delivery, and adoption of AI initiatives across EMEA. Engage senior stakeholders, manage vendors, design training and curricula, apply agile and design thinking, and build organizational AI capability while measuring and communicating delivered value.

Design and architect pragmatic AI solutions using cloud and third-party LLMs; translate business needs into AI-powered systems, prototype POCs, design agent/multi-agent integrations, define standards, and coach teams on Google Cloud AI products.

Leads NVIDIA’s AI Physics sales strategy across EMEA, driving market growth, strategic customer adoption, ecosystem partnerships, pipeline development, and production-scale success across engineering-intensive industries. Builds executive relationships, identifies high-value use cases, communicates market insights internally, and promotes AI Physics, simulation, digital twin, and accelerated computing solutions.

Owns NVIDIA’s EMEA go-to-market motion for its Agentic AI Platform, driving customer and partner adoption across enterprise, cloud, public sector, defense, and regulated environments. Translates complex AI, security, licensing, legal, and deployment requirements into sales plays, reference architectures, field guidance, workshops, and executive content. Coordinates product, engineering, legal, security, sales, and ecosystem teams while sharing market requirements and connecting technical adoption to revenue outcomes.

As a Lead/Staff Full Stack Engineer, you'll design and develop full-stack applications and AI agents for the GenAI platform, focusing on rapid deployment and impactful decision-making for various industries. You'll also mentor engineers and lead major projects.

As a Principal Full Stack Engineer, lead the development of the GenAI platform, design full-stack applications, and mentor engineers. Build scalable systems for AI agents affecting key industries, ensuring security and reliability.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

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.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Design, build, and operate production-grade generative AI systems: multi-agent orchestration, end-to-end RAG pipelines, LLM evaluation and integration, scalable AWS-based AI infrastructure, CI/CD for models, observability, data validation, and evaluation frameworks to ensure quality, attribution, and resilience.