Top Artificial Intelligence Jobs in Cologne

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

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.

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

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 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.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Own product areas within BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, run experiments, monitor feature performance, and iterate for improved user and business outcomes.

Lead product direction and execution for major areas of BJAK's AI Neobank app. Define roadmap, priorities, standards and success metrics across multiple squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement; work across insurance, payments, savings, investing and travel.

Build and scale Jimdo Companion: design agent orchestration, LLM-powered workflows, recommendation engines, APIs and full-stack features; enable AI platform capabilities, collaborate cross-functionally, and drive product-quality AI in production.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered experiences. Guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and translate analytics into product recommendations to shape AI product strategy and best practices.

Map and redesign creative workflows, build and deploy AI-powered content pipelines integrated with DAMs/CMS and MarTech stacks, implement prompt architectures and agent workflows, establish governance and adoption frameworks, deliver proofs-of-concept and production solutions, and enable client teams for sustainable creative transformation.

Embed with client teams to diagnose workflows, redesign operating models, and build AI-powered prototypes (LLM APIs, low-code/no-code) to drive adoption, measure impact, and accelerate ROI through hands-on proofs of concept and change management.

Lead enterprise AI transformation by advising customers, designing agentic AI architectures, delivering rapid POCs, integrating CX/CRM systems, building evaluation and automated evaluation pipelines, and scaling responsible AI solutions while mentoring teams and codifying reusable frameworks.

Build and ship native Android features in Kotlin for an AI-enabled neobank app. Design clean user flows for payments, insurance and financial services, integrate backend APIs, improve performance and stability, use analytics and on-device ML (TensorFlow Lite) where it adds value.

Design, build and ship native Android features for an AI-enabled neobank using Kotlin. Implement secure, performant user flows for payments, insurance and financial services, integrate REST APIs, improve app stability and performance, use analytics to iterate, and incorporate on-device AI where it improves the user experience.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Build production agentic capabilities on ClickStack to investigate incidents, write reusable skills, own the agent stack (context, tools, evals, tracing, cost), create MCP servers/SDKs/integrations, and improve latency, cost, and hallucination handling for AI workloads on a petabyte-scale observability platform.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

The Product Manager will oversee product delivery, integrate AI tools, conduct user research, define success metrics, and coordinate between teams to ensure business success.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

The AI Engineer will develop and deploy AI applications, collaborate on the end-to-end pipeline, create backend services, and develop frontend interfaces.