Top Artificial Intelligence Jobs in Hamburg

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

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.

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.

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

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Join the Compliance team to own AI and e-commerce legal workflows: GDPR and data protection documentation, AI systems inventory, risk assessments, product/feature reviews, contract and IP issue handling, TOS/privacy updates, and monitoring legal developments. Work with the Ethics & Compliance Officer and review uncensored AI content as needed.

The role involves owning legal and compliance workflows related to AI and e-commerce regulations, focusing on contracts, data protection, and risk assessments.

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.

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 improve Restream’s web interfaces and user-facing features for the AI Clips team. Design UI and business logic, collaborate with product designers, participate in code reviews, improve system reliability, and evaluate emerging technologies. The role requires strong JavaScript and TypeScript expertise, advanced React knowledge, CSS preprocessor experience, and familiarity with backend development, cloud infrastructure, containers, and continuous deployment.

Lead enterprise AI strategy, operating model, governance, platform enablement, and CoE to deploy safe, compliant, and scalable AI across R&D, Clinical, Manufacturing, Quality, Commercial, and Corporate functions in a regulated pharmaceutical environment.

Conduct hardware-aware ML research and engineering to accelerate AI inference. Build fine-tuning and post-training pipelines, benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to optimize model quality, latency, throughput, and power on novel silicon.

Lead design and ownership of shared libraries, Spring Boot starters, and agentic frameworks. Set standards for agent orchestration, evaluations, and guardrails. Mentor teams on Python-based agents (LangChain/LangGraph), drive adoption, prototype research directions, and improve developer experience across Kotlin backend and client libraries.

Provide EU-focused counsel on AI and privacy regulation (EU AI Act, GDPR, DSA); draft policies, DPA and contractual terms; translate legal requirements into technical and operational controls; advise product, engineering, and go-to-market teams; liaise with regulators and manage compliance programs and risk assessments.

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.

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.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Provide research and advisory to global CFOs and Controllers on transforming controllership processes (AP, AR, financial close) using AI, automation and finance technologies; produce published research, vendor evaluations, and client-facing presentations; collaborate with analysts and deliver strategic, actionable recommendations to improve financial reporting and automation outcomes.

Lead product development in the industrial sector focusing on IoT, edge computing, and AI applications. Oversee product lifecycle, conduct market research, and collaborate with cross-functional teams.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

As a Staff/Senior AI Product Engineer, you will build AI-powered product features, enhance the tech stack, and design AI workflows while collaborating with product teams. Your focus will be on agent-native architecture and improving the codebase for AI agents.

Seeking a Senior Software Engineer to build AI inference systems. Responsibilities include optimizing GPU kernels, architecting inference stacks, and defining benchmarking methodologies. Requires strong programming skills and experience in ML systems.