Top Artificial Intelligence Jobs in Berlin

Lead presales for complex enterprise deals across DACH and expansion markets: run discovery and technical deep dives, design tailored AI Agent solutions, deliver demos and POCs, represent the company at events, and feed customer requirements back into product and enablement.

Design and build agentic conversational systems combining LLMs, reasoning components, and data pipelines. Lead ML/NLP model training, evaluation, optimization, deployment, and experimentation. Integrate intelligent agents into recruiting workflows, mentor engineers, and improve reliability, personalization, and observability of production ML systems.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

Lead discovery, definition, and delivery of AI-powered B2B SaaS audit products. Talk to customers, prototype with LLMs/agents, work with engineering and design, write clear PRDs, and steward product decisions addressing AI limitations and human-in-the-loop workflows.

Senior audit professional who formalizes ISA/IDW requirements into scalable, auditable AI-driven workflows. Design automated logic for risk ID, contract analysis, independence and documentation per IDW/ISA standards, ensure reviewability, and represent the company in thought leadership and regulatory dialogue.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Build and operate production-grade data, evaluation, and observability infrastructure for LLM agents. Implement online/offline evaluation, quality gates, data pipelines, trace analysis, retention and replay, and observability tooling while working inside the backend and agent architecture to improve reliability, performance, and cost.

Lead technical engagements to design and deliver AI governance and security architectures for enterprise customers. Perform discovery, build solutions using NVIDIA stack (NeMo Guardrails, NIM, Confidential Computing), produce reference architectures and demos, advise on compliance (EU AI Act), and represent NVIDIA at technical events.

Lead and grow Enjins Investor Services: build brand and GTM, run technical AI due diligences, advise PE/VC on AI strategy, maintain investor and portfolio relationships, own product roadmap and sector alignment (climate, energy, mobility). Serve as hands-on technical lead and commercial owner of the practice.

The role involves adapting scripts for dubbing, reviewing translations, evaluating voice performance, and efficiently managing multiple productions based on client demand.

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.

The AI Research Engineer will research and develop novel model architectures, adapt open-source models, and contribute to the technical strategy and culture of the team.

The role requires working closely with customers to design AI-powered solutions, translating business problems into technical plans, and leading solution implementation.

The role involves developing new AI model architectures and adapting existing models, conducting experiments, and contributing to technical strategy in a high-growth research environment.

The AI Solution Engineer will design and implement AI-powered solutions for customers, translating business issues into technical solutions, and collaborating with stakeholders to ensure success.

Lead end-to-end partner digital journey and define strategy for a central AI platform. Drive LLM orchestration, agentic workflows, RAG patterns, API integrations, partner design system ownership, cross-functional alignment, and mentor product colleagues.

Build and own Python ML data pipelines, feature store design, and model serving infrastructure. Implement data quality and drift detection, establish reusable pipeline patterns, and operate production data systems to support ML training and inference.

Develop curriculum for AI implementation course, maintain program relevance, and create educational materials. Inspire students in AI practices.

Lead the roadmap and delivery of AI & Automation initiatives: identify and prioritize scalable automation opportunities, design and implement solutions using low-code, scripts, and LLMs, measure business impact (OPEX, ROI), and drive adoption across teams through stakeholder engagement and change support.

As an Applied AI Engineer, you will implement AI solutions at Upvest, connecting tools to internal systems and coaching teams on AI usage.

As a Principal Forward Deployed Engineer, you'll deploy JetBrains Central in enterprise environments, implement AI governance, mentor teams, and advise customers on AI-native development best practices.

The AI Operations Specialist at Moss will enhance AI adoption by collaborating with teams to implement AI solutions, automate workflows, and drive operational improvements using various AI tools.

Design, build, deploy and maintain agentic AI systems and automated workflows for Sales, AM and CSM. Implement autonomous data orchestration, signal-based data architecture, and forecasting pipelines. Integrate agents with CRM (Salesforce/HubSpot) and BI, monitor performance, implement guardrails, and collaborate with PlatformOps, Engineering, Data and Process teams.

Build full-stack, user-facing AI applications for public procurement: design, implement, and deploy features using the company tech stack, integrate LLMs, own features end-to-end, improve reliability, and collaborate closely with founders and users.