Top Artificial Intelligence Jobs in Cologne

The Senior Agentic AI Engineer will develop cutting-edge GenAI solutions, design high-performance AI modules, and ensure system integration while leading cross-functional teams.

Lead the architectural direction for Generative AI initiatives, establish CI/CD pipelines, mentor engineers, and drive AI experimentation for logistics solutions.

The role involves testing and debugging AI clusters, maintaining hardware, and collaborating with development teams to resolve issues across networking components.

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.

Design, develop, and maintain Python and Go microservices for data ingestion and processing; integrate backend services with BigQuery and cloud data platforms; implement REST APIs; build scalable data processing pipelines using Apache Beam and Dataflow; test and troubleshoot distributed systems; review designs, estimate work, mentor junior developers, and drive performance and reliability improvements.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

The role involves evaluating and reviewing AI-generated outputs, ensuring cultural relevance and accuracy, and providing annotations for model improvement.

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.

Design, train, optimize, and deploy production-ready computer vision models for image and video analytics. Prepare and manage datasets, integrate models into services, ensure performance and scalability, and collaborate with cross-functional teams while maintaining documentation and code quality.

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.

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.

Lead horizon scanning and external intelligence to identify AI and disruptive tech opportunities for R&D. Translate trends into actionable briefs, design and run pilots with stage-gates, and scale successful capabilities into production. Influence senior leadership with strategic foresight, build external partnerships, and embed a future-back innovation culture across cross-functional teams to measurably improve R&D outcomes.

Lead on-site AI transformation for mid-market CEOs: own executive relationships, drive fixed-scope first wins on real data, translate problems into build-ready specs, manage rollout to finance-verified outcomes, and codify methods to scale deployments.

As a Senior Full Stack Engineer for the AI Platform, you will build solutions enabling rapid GenAI agent development and deployment, impacting healthcare and other sectors. Your role encompasses designing full-stack applications, applying secure SDLC practices, and collaborating on impactful AI systems.

Own and prioritise the central AI platform roadmap (agent runtime, evaluation, pipelines, observability). Run experiments, ship v1s or enable product teams, and collaborate with Engineering, Data Science, and leadership to improve AI product speed, quality, and production robustness.

Provide hands-on stabilization of multiple early-stage, often AI-generated codebases: assess, fix bugs, refactor, add missing core functionality (auth, payments), improve performance, implement lightweight testing, and collaborate across teams on fractional hourly engagements.

Own SEO strategy and KPI progress for client accounts, build AI-supported workflows and automations, create repeatable SEO processes, lead client communications and reviews, and drive measurable visibility and business outcomes by combining AI tools and specialist expertise.

Lead design and build a greenfield, AWS-based agentic software delivery platform. Integrate LLMs and agentic coding tools, implement tool orchestration and function calling, deliver end-to-end full‑stack features, evaluate trade-offs between off‑the‑shelf and custom solutions, and drive autonomous engineering capabilities.

Lead full‑stack development of high‑traffic public websites, integrate AI tooling into the SDLC, design scalable backend and performant UIs, maintain cloud infrastructure, CI/CD, monitoring, and testing, and own services under a you‑build‑it‑you‑run‑it philosophy.

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.

Lead QA automation for AI/LLM systems: define validation criteria for non-deterministic models, build automated and performance tests, validate RAG systems, perform API and integration testing, ensure release readiness and trace defects to data/models/requirements.

Lead a cross-functional squad to architect and ship AI-native workflows, integrations, and experimentation infrastructure that scale growth. Build data frameworks, attribution and analytics stacks, run rigorous A/B programs, package repeatable AI enablement toolkits, and collaborate with Engineering to deploy agentic product surfaces.

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.

Participate in a 3–6 month early career AI R&D program assisting in experiment design and execution for LLMs, reinforcement learning and agentic systems. Write and maintain code for data processing and model evaluation, analyze results, explore efficient distillation, speculative decoding and inference optimization, and collaborate with researchers with potential conversion to full-time.