Top Artificial Intelligence Jobs in Munich

Owns an AI-focused financial product vertical from strategy through launch, including roadmap, backlog, metrics, technical design, money-movement correctness, reliability, security, and scalability. Incorporates regulatory requirements such as KYC, sanctions screening, transaction monitoring, privacy, and data residency. Defines responsible AI usage, evaluation, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Lead product direction, roadmaps, standards, metrics, and execution for major areas of KIRA’s AI Investing App. Guide PMs, product owners, designers, and engineers across multiple squads while balancing UX, data, engineering, regulatory, operational, and business constraints. Drive launch quality, post-launch learning, and continuous improvement across financial products including investing, payments, savings, insurance, and travel.

Design AI-powered healthcare product experiences for clinical workflows, including documentation, patient summaries, search, agent workflows, multimodal interactions, uncertainty handling, and approval flows. Lead discovery, prototyping, and user validation; collaborate with ML engineers, product managers, software engineers, and clinicians. Contribute AI interaction patterns and components to the design system, establish AI design principles, and promote trustworthy, high-quality experiences for hospital users.

Develop TYTAN’s growth strategy by evaluating markets, customer segments, acquisitions, investments, and strategic partnerships. Build financial models, business cases, competitive intelligence, and commercial agreements while advising leadership and coordinating across engineering, finance, and legal. The role requires defense or deep-tech exposure, German and English fluency, extensive international travel, and willingness to undergo security vetting.

Own product areas for KIRA’s AI stockbroking and financial-services applications. Define user problems, product strategy, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; launch features; monitor performance; and continuously iterate using data, research, feedback, and production insights.

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

Own AI Investing product areas from user research and problem definition through roadmap planning, launch, measurement, and iteration. Partner with engineering, design, data, operations, compliance, and business teams to simplify financial products, make informed trade-offs, and deliver customer-focused features quickly. Use metrics, feedback, experiments, and production insights to improve outcomes across insurance, payments, savings, investing, travel, and related financial services.

Grow Qure’s user base from scratch by testing acquisition channels, managing budgets, creating marketing assets, building measurable funnels, and partnering with product on activation-focused improvements. The role requires rapid experimentation across paid, organic, community, video, and content channels, using data to optimize acquisition economics and eliminate underperforming efforts.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive intelligence, sales enablement, technical content, and cross-functional collaboration. The role targets enterprise buyers through sales-led motions and requires measurable pipeline impact, strong writing and presentation skills, and comfort working with technical teams. Experience in AI, contact centers, QA, testing, observability, or monitoring is advantageous.

Supports AI and data consulting projects through market sizing, segmentation, pricing, optimization, predictive modeling, KPI development, dashboards, workshops, and executive presentations. The intern analyzes large datasets, develops practical client solutions, communicates complex findings to nontechnical audiences, and contributes to cross-functional strategy teams.

Lead product direction and execution for major areas of BJAK's AI Neobank app. Define roadmap, priorities, standards and success metrics across multiple squads; guide PMs, designers and engineers; translate regulatory and operational constraints into product decisions; drive launch quality, post-launch learning and continuous improvement; work across insurance, payments, savings, investing and travel.

Build AI-powered features, LLM integrations, and multi-step agent workflows for requirements and systems engineering software. Integrate agents with internal data sources, reason over requirements documents and system specifications, address security and data privacy, and deploy AI capabilities into production. The role requires software engineering experience, hands-on LLM integration, end-to-end ownership, and collaboration in a startup environment.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Join Welo Data’s global contributor network for flexible, remote AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and requirements provided before each assignment.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Assess and evaluate AI-generated technical content, craft and answer advanced ML/AI questions, identify errors and hallucinations, rank responses by correctness and clarity, and provide precise feedback to improve model performance.

Own the technical design and production delivery of multi-component AI systems. Build evaluation frameworks for nondeterministic behavior, data pipelines, feedback loops, model-tool boundaries, and agent harnesses. Optimize latency and cost through streaming, caching, routing, and prompt design. Develop internal tooling, assess external models and datasets, support APIs and SDKs, mentor engineers, and participate in 24/7 on-call operations.

Lead the Applied AI Architect team across the DACH region, defining technical strategy and operating models for strategic enterprise customers. Oversee customer journeys from discovery through production adoption, coach architects on AI and enterprise architectures, build executive relationships, allocate technical resources, and coordinate cross-functional specialists. Establish portfolio health metrics, drive measurable customer value and growth, and translate field insights into product and go-to-market improvements while promoting responsible AI deployment.

Build and operate Typeform’s internal AI Operations capability for R&D. Identify and prioritize AI opportunities, discover workflows, design and deliver AI-native solutions, and manage their lifecycle after launch. Responsibilities include improving enterprise AI tools, coordinating access and governance, evaluating reliability and security, supporting automations and agents, measuring adoption and value, and partnering with Product, Engineering, IT, InfoSec, Finance, and other teams.

Develop and implement GEO strategies that improve client visibility across AI search environments, LLMs, AI assistants, and generative platforms. Analyze AI visibility, citations, sentiment, Share of Voice, and rankings; turn insights into recommendations, media plans, budgets, reports, and client presentations. Onboard clients, support pitches, manage relationships, collaborate cross-functionally, and develop GEO playbooks, training, and best practices.

Citrine Informatics invites applicants to submit resumes for general opportunities. They review applications periodically for potential new roles in AI-driven materials and chemicals.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.