Top Artificial Intelligence Jobs in Cologne

Assess client engineering workflows, design and build agentic tooling and processes, instrument solutions for cost and business outcomes, run demos and enablement sessions, support teams to independent operation, and contribute technical leadership to presales when needed.

Own and evolve production voice AI systems across telephony, speech, and LLM stacks. Build, ship, and scale phone agents; implement observability, evaluation, and tooling; collaborate cross-functionally to integrate voice AI into product workflows.

Lead analysis and improvement of conversational AI systems by designing prompt strategies, optimizing RAG and retrieval, evaluating LLMs, defining AI quality metrics, running experiments, and collaborating with engineers to deploy production-scale improvements.

Design, build, and productionize AI features including conversational and voice AI. Integrate LLMs and AI APIs, implement RAG and agent workflows, troubleshoot production issues, optimize performance and costs, and collaborate with product and AI science teams to deliver scalable, reliable AI systems.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Lead rapid end-to-end prototyping of LLM-powered agent systems: build working prototypes, design experiments measuring quality/latency/cost/user impact, validate with users, produce reference implementations, and hand off to product teams to inform roadmap decisions.

Build and scale AI-driven automation solutions using LLMs and agent frameworks. Design multi-step workflows, integrate AI with internal systems (HR, finance, legal, ticketing), automate document processing and data extraction, and train internal teams. Collaborate with technical leads, product, and IT to deliver practical, production-ready automation and iterate quickly.

As an AI Product Manager at n8n, you'll develop AI-driven product experiences, coordinate with various teams, and innovate through user-centric design, focusing on AI trust and building.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

The role involves evaluating AI outputs, correcting texts, analyzing multimedia, and providing culturally relevant content while adhering to guidelines.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

Design and build agentic conversational systems combining LLMs, reasoning components, and data pipelines. Lead ML/NLP model training, evaluation, optimization, deployment, and experimentation. Integrate intelligent agents into recruiting workflows, mentor engineers, and improve reliability, personalization, and observability of production ML systems.

Lead presales for complex enterprise deals across DACH and expansion markets: run discovery and technical deep dives, design tailored AI Agent solutions, deliver demos and POCs, represent the company at events, and feed customer requirements back into product and enablement.

Lead AI architect role supporting EMEA consulting: develop reusable AI consulting solutions, support sales with technical discovery and scoping, run in-depth workshops, and act as lead architect on complex AI projects. Drive adoption of Red Hat AI technologies, enable regional teams, and ensure solutions meet customer ROI while handling sensitive customer information.

Build and ship production-grade AI features and agent/LLM-powered workflows for analytics and data engineering. Rapidly prototype, iterate with users, integrate with internal tools, and ensure scalable, observable solutions.

Lead end-to-end organic growth programs for B2B tech clients, owning strategy, execution, deliverable quality, renewals, and commercial-impact metrics across a portfolio of up to ten accounts. Prioritize work, set and keep commitments, evaluate recommendations, and partner with a production team that executes briefs, decks, and data.

The role involves driving new AI software sales, engaging with executives, and leveraging industry expertise to close large enterprise deals.

Design rigorous, deterministic advanced mathematics problems and provide complete verified solutions to train and evaluate state-of-the-art AI models. Produce reproducible, well-documented reasoning and, when needed, computational workflows using Python and scientific libraries. Ensure problems target deep mathematical reasoning and are technically precise.

The role involves integrating AI tools into .NET and C++ applications, optimizing models for performance, and collaborating with engineers on deployment and monitoring.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Build production-grade AI agents, LLM apps, RAG systems and automations for fintech workflows (onboarding, KYC, support, risk, CRM). Work with product and engineering to ship, evaluate, and improve reliable, secure, cost-effective AI-enabled systems that replace manual processes.