Top Artificial Intelligence Jobs in Hamburg

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.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

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.

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.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Lead customer-facing implementations of AI-driven chatbots, voicebots, and multimodal assistants on the NICE CXone/Cognigy platform. Configure NLU/ASR/IVR, integrate CRMs and APIs, design conversational flows and personas, run workshops, support UAT and go-live, produce documentation and demos, mentor team members, and optimize post-deployment performance.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Review, evaluate, and annotate AI-generated multilingual and multimodal content (text, images, audio, video). Perform quality checks, identify edge cases, participate in calibration and alignment, and provide feedback to improve datasets, workflows, and annotation guidelines.

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.

Lead end-to-end development and integration of AI features (LLMs/ML) into web/mobile apps and APIs. Design AI-first UX, craft and optimize system prompts, build agentic workflows (LangChain/CrewAI/AutoGen), implement error-handling and hybrid cloud/on-prem deployments, and collaborate within product squads using Agile/Scrum.

Design and implement CI/CD and IaC for LLMs, automate model and dataset versioning, provision GPU/TPU infrastructure, enable safe experiment gates and progressive delivery, and build monitoring/observability and feedback loops for production LLM endpoints.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.