Top Artificial Intelligence Jobs in Munich

Leads OKX’s global marketing compliance function, building policies, controls, review and approval processes, monitoring programs, and regulatory change management for advertising, promotions, social media, influencers, affiliates, and product launches across CeFi and DeFi. Partners with Marketing, Product, Legal, Risk, and Compliance teams; manages incidents, supports audits, delivers training, and scales the compliance team and framework.

Build and own automation systems for multi-market, multilingual SEO, including content pipelines, reporting, QA, monitoring, and AI-powered workflows. Integrate GSC, GA4, BigQuery, CMS platforms, and SEO tools; analyze crawl budgets and log files; diagnose indexability, rendering, and tracking issues; and collaborate with SEO, engineering, security, legal, and data teams. Write production code, document systems, and ensure reliable operations across markets.

Lead SAP’s AI technology strategy and enterprise architecture for AI-native systems. Guide adoption of LLMs, RAG, multi-agent systems, agentic orchestration, evaluation, and responsible AI practices. Align platform and application teams, conduct architecture reviews, set CI/CD and MLOps standards, mentor senior architects, engage executives, and drive secure, high-quality AI adoption across SAP’s product portfolio.

Leads NVIDIA’s EMEA public-sector AI business development strategy, collaborating with sales, marketing, solution architects, developer relations, and partners. Responsibilities include sales enablement, account planning, pipeline and revenue growth, customer engagement, opportunity reviews, government lighthouse-account development, partner coordination, case studies, and AI platform adoption. The role also mentors developer relations teams and communicates technical AI value to government customers and stakeholders.

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.

Leads NVIDIA’s AI Physics sales strategy across EMEA, driving market growth, strategic customer adoption, ecosystem partnerships, pipeline development, and production-scale success across engineering-intensive industries. Builds executive relationships, identifies high-value use cases, communicates market insights internally, and promotes AI Physics, simulation, digital twin, and accelerated computing solutions.

Owns NVIDIA’s EMEA go-to-market motion for its Agentic AI Platform, driving customer and partner adoption across enterprise, cloud, public sector, defense, and regulated environments. Translates complex AI, security, licensing, legal, and deployment requirements into sales plays, reference architectures, field guidance, workshops, and executive content. Coordinates product, engineering, legal, security, sales, and ecosystem teams while sharing market requirements and connecting technical adoption to revenue outcomes.

Own external security audits, gap analyses, risk assessments, compliance frameworks, IAM and supplier management domains. Maintain security procedures, execute controls, coordinate control owners, handle incidents, answer security and privacy questions, and lead security improvement projects across IT and software development. Support secure AI adoption and onboard acquired companies into the global security and compliance program.

Leads research and advisory work for CIOs on data, analytics, and AI strategy, governance, operating models, architecture, value realization, and emerging technologies. Develops thought leadership, market analysis, presentations, and actionable client guidance; evaluates agentic and composite AI, modern data platforms, AI-ready data, organizational maturity, and capability-building needs. Represents Gartner at client meetings and conferences, supports sales, mentors colleagues, and shapes research agendas.

Build and scale low-latency AI serving infrastructure for SLMs, prompt-injection engines, and classification services. Architect GPU-accelerated inference pipelines, backend microservices, Kubernetes clusters, rate limiting, tenant guardrails, monitoring, and autoscaling systems. Develop continuous evaluation, retraining, model CI/CD, regression gates, and MLOps/LLMOps pipelines while improving service reliability, latency, cost efficiency, and detection quality.

Support ML/LLMOps and AI agent infrastructure for model evaluation, testing, and deployment on AWS. Evaluate LLM frameworks, agent architectures, APIs, and RAG pipelines; develop evaluation systems measuring reliability and reasoning quality; automate machine learning and data pipelines; and document findings. The internship also provides experience with critical infrastructure, AI-powered applications, software engineering, and end-to-end machine learning projects.

Leads the AI product portfolio for fitness apps and connected gym equipment. Responsibilities include managing product managers, defining AI product strategy, prioritizing multi-team roadmaps, owning cross-platform user experiences, partnering with engineering and data teams, establishing responsible AI evaluation practices, and driving adoption and business outcomes. Requires strong product leadership, AI/ML product delivery experience, stakeholder management, and strategic execution in complex organizations.

Drive company-wide AI adoption by deploying LLM chat and automation tools, partnering with leadership to prioritize use cases, delivering workshops and coaching, tracking adoption metrics, and identifying opportunities to improve workflows. The role requires hands-on experience implementing AI in organizations, strong business judgment, practical automation skills, and the ability to translate team needs into effective AI solutions.

Leads customer AI transformation initiatives by designing and implementing Microsoft Copilot, agentic AI, and cloud solutions. Acts as a trusted technical advisor, drives adoption and business value, develops Copilot prompts and agents, supports customer success and renewals, identifies expansion opportunities, and contributes reusable intellectual property and customer insights.

Leads a 10+ person AI-native R&D engineering team while remaining hands-on with coding, architecture, prototypes, distributed systems, and production readiness. Owns initiatives from ambiguous requirements through implementation, evaluation, validation, documentation, and handoff. Establishes responsible AI-assisted development practices, mentors engineers, maintains high standards for security, testing, reliability, and code quality, and partners with Product, Architecture, Security, and Engineering stakeholders.

Support client projects by structuring business problems, assessing data, selecting analytical approaches, conducting analyses, building and validating models, and translating findings into recommendations. The role involves quantitative consulting across industries, collaboration with clients and project teams, and potential work in AI, credit risk, sustainability, optimization, GenAI, and supply chain analytics. Business fluency in German and English and willingness to travel are required.

Supports client projects by structuring analytical problems, assessing data, selecting analytical methods, conducting analyses, building and validating models, and translating results into recommendations. Work may involve AI, predictive modeling, portfolio decarbonization, credit risk, optimization, chatbots, and supply-chain analytics. The role also includes client collaboration, communicating complex concepts clearly, and contributing to project teams across industries.

Combines hands-on AI engineering with technical enablement. Designs and delivers customer workshops, employee onboarding, partner training, and certification programs focused on building AI agents and applications on the Wonderful platform. Participates in live customer projects, teaches architecture and integrations, and continuously improves curricula based on feedback, product changes, and field experience. Requires strong AI engineering, instructional design, facilitation, communication, and enterprise software experience, plus fluency in German and English.

Build and operate production AI products, including sales acceleration systems, recommendation engines, and agentic operating systems. Own solutions end to end by gathering user needs, designing, coding, deploying, monitoring, and maintaining them. Evaluate agent decisions, detect performance drift, enable non-engineers to create automations, and contribute to shared AI infrastructure. Work across TypeScript, Rust, Ruby, Google Cloud, Kubernetes, and infrastructure as code while collaborating across advisory, sales, operations, and engineering.

Design, implement, review, and validate production-grade Rust backend systems for AI training projects. Responsibilities include API development, persistence, asynchronous and concurrent processing, performance optimization, reliability testing, debugging, architecture assessment, and creating reproducible workspaces and containerized test environments. The role requires clear technical documentation, evaluation of edge cases and failure scenarios, and consistent delivery of approximately 15 hours of weekly project work.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic systems. Designs secure end-to-end architectures, integrates Claude and other models with ServiceNow, APIs, databases, cloud platforms, and workflows, and leads discovery, proofs of concept, and client delivery. Develops ML and NLP solutions, data pipelines, dashboards, evaluation frameworks, observability, governance, and responsible AI controls. Collaborates with stakeholders, engineers, consultants, and clients to operationalize reliable AI applications.

Build and operate production AI products using generative AI, LLMs, RAG, vector search, and agentic systems. Develop scalable machine learning services, pipelines, APIs, infrastructure, and automated evaluation systems. Improve AI quality, reliability, security, performance, and cost while establishing engineering standards. Collaborate with Product, Engineering, Data Science, Data Engineering, and Analytics teams to deliver dependable customer experiences.