Top Artificial Intelligence Jobs in Munich

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

Design polished, user-centric mobile and web interfaces for BJAK's AI-driven financial products. Translate requirements into wireframes, flows, prototypes and high-fidelity UI. Maintain design system, ensure accessibility and responsiveness, iterate using user feedback, and collaborate with PMs and engineers to deliver pixel-perfect implementations.

Develop and ship features for client projects and in-house products (including mobile AI apps) using TypeScript, React, and Node.js. Work agent-first with AI coding tools, review and harden generated code, debug unfamiliar code, turn loose requirements into working software, and own small end-to-end pieces with senior mentorship.

Work directly with the CTO to build and ship fullstack features to production daily. Contribute to a fully agentic system automating the headhunting process end-to-end, live-working from hackerhouses in Latin America alongside a small founding team.

Design and build backend systems powered by LLMs, develop AI agent workflows and tool integrations, implement RAG pipelines with vector search, handle prompt orchestration and context management, optimize performance and cost, and ship AI features to production.

As a Staff/Senior AI Engineer, you will develop production-ready coding agents and improve AI model capabilities for JetBrains' software development tools. Collaborating with teams, you'll focus on model training, evaluation, and product integration to enhance user experience.

The Applied AI Architect will advise enterprise clients on integrating AI solutions, develop scalable architectures, and support their adoption journey of Claude AI systems.

Lead the core biometric recognition technology team, managing the full lifecycle of ML systems focused on iris and face recognition while ensuring privacy and reliability at scale.

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Lead company-wide AI initiatives: identify high-impact automation use cases, design and implement AI-powered workflows using Make, Claude and Pinecone, collaborate cross-functionally, measure impact, and enable teams to adopt and optimize AI-driven processes.

Develop, maintain, and lead software platforms, tools, and infrastructure for regulated computational pathology products. Implement production-quality features, follow V-model regulated development practices, support risk assessments, establish engineering controls and documentation, collaborate cross-functionally, provide technical leadership, and mentor engineers to ensure compliant, traceable, and scalable AI-enabled medical software delivery.

Design, implement, and maintain software platforms, tools, and infrastructure for regulated AI-enabled computational pathology products. Ensure compliance with regulated development practices (V-model), support risk assessments, verification/validation, traceability, and lifecycle documentation. Provide technical leadership, process improvements, and mentorship across multidisciplinary teams to enable scalable, compliant AI software delivery.

Own end-to-end development of AI-powered product features: design prompts, tools, memory and agent workflows; deploy and stabilize LLMs in production; optimize latency, cost, and reliability; build evaluation frameworks; debug model, orchestration, infra, and UX issues; and collaborate with product and engineering to deliver measurable improvements.

Build end-to-end AI-native product features across frontend, backend, and AI integrations. Design and deploy reliable agent workflows, integrate LLMs/memory/tools, optimize streaming low-latency interactions, improve observability, fallback and recovery mechanisms, and iterate on production failures to increase reliability of multi-step workflows.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior HPC and AI Network Software Architect will develop scalable AI systems focused on distributed training and inference, collaborating on software and hardware integration to enhance performance.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Lead design and delivery of AI-enabled SDLC platforms for embedded software: orchestrate multiple GenAI systems and agent frameworks (LangChain, AutoGen, CrewAI), integrate deeply with Atlassian tools (Jira, Confluence, Bitbucket) and CI/CD pipelines, ensure governance, scalability, and compliance with MISRA, ISO 26262, ISO/SAE 21434 and ASPICE.

Develop computer vision models for object recognition and video understanding while evaluating and improving their performance for real-world applications.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Design and implement GenAI solutions for clients and internal use. Develop multimodal agents, automate workflows, and build scalable deployment tooling using Python, Databricks, SQL, Docker, LangGraph, and cloud platforms (Azure/AWS/GCP). Document processes, present to stakeholders, and stay current on ML and generative AI techniques while collaborating with the AI team.

Build and scale AI-driven automation solutions using LLMs and agent frameworks. Design multi-step workflows, integrate AI with internal systems (HR, finance, legal, ticketing), automate document processing and data extraction, and train internal teams. Collaborate with technical leads, product, and IT to deliver practical, production-ready automation and iterate quickly.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.