Top Artificial Intelligence Jobs in Berlin

Lead full‑stack development of high‑traffic public websites, integrate AI tooling into the SDLC, design scalable backend and performant UIs, maintain cloud infrastructure, CI/CD, monitoring, and testing, and own services under a you‑build‑it‑you‑run‑it philosophy.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Lead QA automation for AI/LLM systems: define validation criteria for non-deterministic models, build automated and performance tests, validate RAG systems, perform API and integration testing, ensure release readiness and trace defects to data/models/requirements.

Lead a cross-functional squad to architect and ship AI-native workflows, integrations, and experimentation infrastructure that scale growth. Build data frameworks, attribution and analytics stacks, run rigorous A/B programs, package repeatable AI enablement toolkits, and collaborate with Engineering to deploy agentic product surfaces.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Customer-facing ML/GenAI engineer who advises customers, runs demos and strategic reviews, drives product adoption, partners with sales and engineering, and helps expand accounts by implementing best practices for production ML/LLM systems.

Partner with EMEA AI/ML teams to drive adoption of Arize's observability platform, manage accounts through onboarding and renewals, run workshops/QBRs, identify upsell opportunities, and coordinate technical resources to ensure customers achieve measurable AI outcomes.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Designs and implements AI, automation, and analytics solutions for supply chain. Develops data models, governance, reporting, and advanced analytics; collaborates with process owners to enable data-driven decisions and trains/supports business users.

Lead deployment and scaling of AI/ML models, agentic tools, and automation across R&D. Drive MLOps, platform integration, compliance, stakeholder partnerships, capability building, end-user support, and metrics tracking to deliver measurable value.

Produce narrative-driven long-form AI-generated videos end-to-end: concept, prompting, generation, editing, compositing, motion graphics, color correction, and maintaining character/style continuity using emerging AI video tools.

Manage the GTM AI Engineering team overseeing automation, AI agent strategy, and integration with sales and marketing for revenue growth.

As a Senior Forward Deployed Engineer, you'll deliver AI solutions, engage with customers, and lead project lifecycles, including deployment and integration.

Looking for a Mid/Senior AI Cinematic Video Editor to create narrative-driven video content using AI tools, managing the entire production process from concept to final delivery.

As a Voice AI engineer, you will build reliable systems, solve unique Voice AI challenges, and evaluate models for customer impact, taking ownership of projects.

The Senior GTM Engineer at Xentral will develop AI-driven workflows, ensure efficient data flows, integrate marketing tools, and create feedback loops to enhance marketing processes.

The Senior Developer Relations Manager will drive AI technology adoption by engaging with ISVs and enterprise leaders, provide technical leadership in AI and computer vision, and lead thought leadership discussions for NVIDIA's AI ecosystems.

Lead the design and deployment of AI and automation solutions, focusing on scalable applications, automation of workflows, and integration of LLMs in operational processes.

The AI Engineer will develop and maintain AI infrastructure, implement ML solutions, curate datasets, and collaborate with teams to enhance products.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.