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.

Lead storytelling and communications for Pfizer's AI-native engineering transformation: set strategy, create content and live events, manage budgets and agency relationships, build cross‑functional engagement, track success metrics, and lead a storytelling team to translate engineering work for executives and broader audiences.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

Lead research and deliver executive-level insights on enterprise AI strategy, producing thought leadership, advising clients and vendors, supporting sales and conferences, mentoring analysts, and driving research agenda and process improvements to accelerate clients' AI value realization.

The Account Executive will establish and scale the market presence in Austria, acquiring customers, managing success efforts and owning the full sales 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.

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.

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.

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.

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.

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.

Define and deliver AI strategy, architecture, and standards. Design production-ready solutions (retrieval, orchestration, model serving, evaluation). Implement MLOps/LLMOps, governance, compliance (GDPR), and embed AI into products working with product, engineering, data, and platform teams.

Build and ship in-product AI capabilities: integrate LLMs and RAG, design prompting and evaluation, implement MLOps/LLMOps, ensure production reliability, privacy, and compliance, and collaborate with data, platform and product teams to move AI from prototype to production.

Lead the planning, execution, and delivery of robotics projects, coordinating cross-functional teams and managing budgets, timelines, and risks.

Own product and go-to-market strategy for GPU and AI services: define roadmap, pricing, packaging, and target segments; convert customer research into engineering backlogs; act as technical lead for enterprise accounts; monitor competitors and work with sales and channel partners to scale commercial adoption.

Lead end-to-end development of production-ready AI and LLM solutions: identify use cases, design architectures, implement and deploy models (RAG, embeddings, fine-tuning), experiment with new models and tools, collaborate with clients and stakeholders, and mentor junior team members.

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.

Design, build, and operate scalable AI/ML platforms: training, inference, pipelines, feature stores, MLOps automation, GPU optimization, monitoring, security/governance, LLM evaluation, RAG and agent development, and cross-functional enablement to productionize models.

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.

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.

Lead and execute the company-wide AI roadmap, enable teams to use AI tools, identify and prioritize AI initiatives across the value chain, advise executive leadership, grow the AI Champions network, ensure governance/security/compliance, and scale AI opportunities organization-wide.

Architect and implement scalable, robust systems for ML-driven scientific research. Build and optimize distributed data-processing and model-training pipelines on cloud or HPC with GPUs/tensor accelerators. Develop tools to train, optimize, and scale ML solutions, collaborate with scientists and engineers, document best practices, and maintain high code quality.