Top Artificial Intelligence Jobs in Cologne

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.

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.

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.

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.

Lead full-cycle enterprise sales for an Energy AI platform: engage C-level and technical stakeholders, build data-driven pipeline, refine GTM and sales playbook, run demos, negotiate and close high-value deals, provide customer feedback to Product/Engineering, and use modern tech/automations to scale revenue.

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

Design rigorous, deterministic advanced physics problems and provide complete verified solutions for AI training and evaluation. Create reproducible simulations or computational workflows (using Python and scientific libraries), document reasoning clearly, and ensure technical precision. Contribute to problem design that tests deep multi-step physical reasoning.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Own the end-to-end process converting product, marketing, and account signals into qualified sales opportunities. Build qualification criteria, routing rules, data enrichment and automation, partner across product/marketing/sales/analytics, create feedback loops, and measure and optimize funnel conversion and data quality.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

Design and implement agentic tools, translating product needs to production, scale prototypes, improve semantic search, build evaluation pipelines.

Lead design and delivery of AI-powered features for CLion and C++ workflows. Architect AI integrations and agent tools, prototype and validate debugging, code-understanding, refactoring, and navigation workflows, build benchmarks and evaluation frameworks, and collaborate with product, ML, QA, and engineering teams to set technical direction and roadmap for AI-assisted C++ development.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Lead development of an autonomous agentic AI scientist for drug discovery: architect multi-step reasoning agents, bridge AI and wet-lab experiment design, prototype to productionize agentic systems, publish research, and collaborate with biological experts.

The Senior Product Manager will own the strategic roadmap, lead market research, manage monetization strategies, and mentor product managers within an AI and EdTech environment.

Author and refine a systems-engineering curriculum for experienced engineers: shape syllabus, define learning objectives, produce and fact-check lessons using AI tools, design hands-on projects and rubrics, and iterate with LX designers to meet milestone deliverables over a 4–6 month, ~15–20 hr/week engagement.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Own and evolve ClickHouse's Python connector and SDK ecosystem, build integrations with orchestration and transformation tools, drive AI/LLM integration strategy for RAG and feature pipelines, engage with open-source community, and collaborate across product and engineering to prioritize real-world data workflows.

Partner with customers and internal teams to define, design, and deliver high-volume conversational AI agents. Translate business needs into agent designs, lead multiple complex deliveries, bridge technical and business stakeholders, and iterate using performance data to improve outcomes and adoption.

Own and scale AI enablement: build core orchestration, LLMOps/MLOps platforms and governance, integrate generative AI into product, optimize cost/latency, run PoC-to-production delivery, upskill teams, standardize tooling and vendor strategy, and track adoption/ROI metrics.