Top Artificial Intelligence Jobs in Hamburg

Lead global sales strategy for digital flexo packaging solutions: set targets, build and manage a high-performing sales team, drive channel partnerships, forecast and budget, analyze CRM data, engage key clients, and collaborate with product, marketing, and global accounts to scale enterprise CAPEX sales and roadmap adoption.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Citrine Informatics invites applicants to submit resumes for general opportunities. They review applications periodically for potential new roles in AI-driven materials and chemicals.

As a Senior Sales Engineer, you will advise healthcare organizations on interoperability, analytics, and AI solutions, supporting technical sales throughout the cycle.

The Senior Solutions Engineer collaborates with customers to design and sell innovative internet solutions, manage technical queries, and support internal teams, focusing on cloud and security technologies.

The role involves designing and deploying AI systems for clinical operations, developing models for decision-making, and ensuring AI implementation aligns with data governance standards. Responsibilities include collaborating across teams, enhancing operational efficiency, and advancing AI applications in a regulated environment.

Lead Hapag-Lloyd's AI Lab to discover, incubate, and mature AI opportunities into investment-ready business cases and MVPs. Drive awareness, workshops, and stakeholder engagement across the organization, assess impact, and build external partnerships to scale AI solutions.

Lead and operate the sales process for AI and robotics solutions: identify and qualify leads, run initial customer discussions, manage pipeline and proposals, coordinate with technical teams, hand over projects to project management, and collect market feedback to shape product and business development.

Design and ship production generative AI systems and multi-agent solutions for enterprise clients. Build LLM and RAG pipelines, vector search, and AI-driven automations integrated into cloud-native SaaS architectures. Collaborate with product, engineering, and leadership; advise clients on AI strategy and deployment, focusing on security, scalability, and measurable business outcomes.

Develop and deploy robust perception and AI systems for industrial robotics. Implement, train, and debug PyTorch models (primarily computer vision), prototype research, integrate solutions with camera systems and production workflows, and optionally contribute NLP/LLM functionality.

Design, implement and optimize Retrieval-Augmented Generation platforms, document ingestion, embeddings and vector search. Build agentic workflows with LangGraph, integrate LLM providers and cloud services, containerize and automate infrastructure, implement testing and validation for RAG systems, and develop REST APIs and data models.

Manage and coordinate software integration projects in production: gather and analyze customer requirements, prioritize Jira tickets, shape product roadmap, integrate computer vision/AI/3D/LLM technologies, ensure onboarding and customer experience, run planning meetings, support sales/trade shows, and drive business development.

Lead a team of Data and AI engineers, develop AI solutions, design cloud infrastructure, and streamline data processes. Collaborate with stakeholders to align AI efforts with business goals.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead Data & AI consulting engagements from strategy to delivery: identify high-impact use cases, define roadmaps, guide bespoke AI and platform development, manage stakeholders and project delivery, support sales and proposals, and create reusable assets while representing the discipline externally.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

The VP of Business Development will drive commercial growth in Europe, focusing on strategic opportunities with renewable energy stakeholders and building relationships to enhance market presence.

Design and implement execution primitives for AI/agent workflows in Kestra's Java core, build and maintain plugins to connect LLM providers and agent frameworks, convert agent use cases into reusable blueprints with product, and ensure AI executions are observable, debuggable, correct, and reliable.

Own AI workflows product: discover and validate AI use cases with customers, define roadmap and specs, create reusable blueprints with engineering, and prioritize what not to build.

Build end-to-end AI features and prototypes across backend and frontend, prototype AI agent use cases, create UI/blueprints/examples to make AI workflows usable, and ship features quickly in collaboration with product and backend teams.

Lead and own Agent0: co-own roadmap with Product, hire and grow a senior AI engineering team, drive delivery of production-grade agentic AI, set evaluation and monitoring standards, prototype innovations, and provide thought leadership on AI production excellence.

Evaluate and rank personalized LLM responses using multi-turn prompts based on your personal data sources. Design prompts, assess grounding, integration, and helpfulness, perform side-by-side comparisons, write clear rationales with turn references, extract and verify debug info, and maintain strict data hygiene. Provide constructive feedback and detailed annotations while working independently as part of a global evaluation team.

Coordinate day-to-day production between key accounts and production teams. Break projects into tasks, track progress, flag delays, ensure complete briefs, manage client feedback and approvals, and oversee multiple clients and formats in parallel. Operational role focused on execution with fast turnarounds; on-site presence in Hamburg required three days weekly.