Top Artificial Intelligence Jobs in Munich

Translate TecAlliance business problems into production-ready AI solutions: define use cases, prototype with ML/LLM components, iterate with stakeholders, and hand over to production while documenting and scaling best practices.

Lead MLOps Engineer responsible for building and automating the lifecycle of multimodal agentic AI: orchestration (LangChain/LangGraph), training pipelines for LLMs and CV, containerized model deployment, low-latency inference, model monitoring/drift detection, and optimizing ML infrastructure for performance and cost.

Serve as a forward-deployed technical partner translating customer workflows into scalable AI deployments. Drive adoption, own end-to-end deployments and integrations, build prototypes and demos, troubleshoot solutions, design integration architecture, and feed customer insights to product and engineering teams to scale enterprise adoption.

As an AI Solutions Engineer, you will design and implement AI-driven automations to enhance workflows and eliminate bottlenecks, collaborating across teams to innovate solutions.

Lead and grow a new AI Engineering & AI Platform team to turn AI/LLM concepts into production-grade, cloud-native backend systems and platform services. Provide technical direction across APIs, AWS/Terraform infrastructure, CI/CD, observability, governance, cost control, and agentic workflows while collaborating with Data Science, Product, Security, and stakeholders to deliver scalable AI use cases.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

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.

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.

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.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

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.

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.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Design, build, and operate production-grade generative AI systems: multi-agent orchestration, end-to-end RAG pipelines, LLM evaluation and integration, scalable AWS-based AI infrastructure, CI/CD for models, observability, data validation, and evaluation frameworks to ensure quality, attribution, and resilience.

Design, build, and deploy production AI agents and backend services. Integrate with ERPs and external APIs, handle structured and unstructured data, implement memory/retrieval and orchestration pipelines, and maintain cloud infrastructure, observability, CI/CD, and robustness for scalable agent workloads.

As a Senior Full Stack Engineer for the AI Platform, you will build solutions enabling rapid GenAI agent development and deployment, impacting healthcare and other sectors. Your role encompasses designing full-stack applications, applying secure SDLC practices, and collaborating on impactful AI systems.

Uncountable seeks a Platform Engineer focused on Generative AI to develop AI-powered tools for scientific R&D, requiring fullstack development skills and a passion for advancing user needs.

Lead GenAI platform introduction, enablement, and governance across client and internal projects. Drive adoption through strategy, training, advocacy, and community-building. Manage vendor relationships, guide technical integration on cloud platforms, establish governance, ensure secure compliant use, mentor junior staff, and collaborate with IT, legal, security, and business units to deliver scalable AI solutions.

Lead AI and cloud architecture for EMEA: identify and prioritize AI automation use cases, adapt global AI products, assess feasibility and ROI, design data products and scalable data pipelines, establish AI/data governance, and shape cloud, data, and AI architecture to deliver measurable business value.

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.

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.

Partner with government customers and partners to design, prototype, and deploy AI solutions (computer vision, LLMs, generative/agentic AI). Create technical collateral, lead hands-on trials and deployments using NVIDIA hardware/software, and advocate product requirements across product, sales, and engineering teams.

Own product areas within BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, run experiments, monitor feature performance, and iterate for improved user and business outcomes.