1087 germany English-speaking jobs in Bavaria

. * Valid residence permit in Germany. ## What We Offer * Performance-based generous compensation and ... earning potential. * Flexible work schedule and ability to work from home. * 'In Office’ locations within Germany

Since 2000, Tieto has been represented in Germany. ... With about 100 experts, Tietoevry Germany is committed to develop enterprises and society through IT

Since 2000, Tieto has been represented in Germany. ... With about 100 experts, Tietoevry Germany is committed to develop enterprises and society through IT

Since 2000, Tieto has been represented in Germany. ... With about 100 experts, Tietoevry Germany is committed to develop enterprises and society through IT

Develop and operate AI platforms for data pipelines, training, deployment, and inference. Optimize AI models and lead technical solutions for scalable AI platform features. Ensure reliability, performance, and cost-efficient operation of AI-based systems.

Define AI system strategy for ADAS, including model architectures, data flows, and integration concepts. Develop prototypes, evaluation pipelines, and data models, testing against real-world product requirements. Collaborate with expert teams to co-create cutting-edge ADAS products spanning L2 to L4 autonomous driving.

Develop and industrialize end-to-end machine learning algorithms for ADAS functions. Design and improve human-like driving behavior using behavior models, reinforcement learning, and hybrid data- and model-driven approaches. Execute full training and evaluation cycles, from data selection and scene analysis to error diagnosis, validation of new function states for various markets and ODD extensions.

Design end-to-end AI and data architectures for vehicles, focusing on edge computing and diverse use cases. Build scalable AI software platforms using the latest technologies and leverage in-house software stacks. Define interfaces between platforms and applications, ensuring new requirements are integrated into AI and data platforms.

Builds and maintains end-to-end ML pipelines, including data ingestion, training, evaluation, and deployment. Engineers petabyte-scale data pipelines, transforming raw automotive data into training formats. Develops and maintains model compilation pipelines for in-vehicle hardware. Operates observability stacks, providing dashboards, alerts, and log aggregation.

Validation Engineer will build and operate cloud-based validation environments for a multi-agent AI system. Responsibilities include designing AI-assisted exploration strategies, developing coverage models and metrics, establishing validation methodologies, and building observability tooling.

As a Senior AI Platform Security Engineer, you will define architecture standards, security policies, and SLAs/SLOs for an AI Moderation Layer. You will lead the design of a multi-layered defense strategy to protect against AI threats and establish and improve the security lifecycle for LLMs/Agents. You will collaborate with legal, compliance, and data protection teams to ensure regulatory alignment for AI security. You will also mentor and guide an agile team of engineers and data scientists, fostering best practices in architecture, security, and platform engineering.

Develop and operate scalable AI platforms in cloud environments. Support the entire AI lifecycle, from data preparation to deployment. Automate infrastructure and workflows using DevOps and MLOps tools. Collaborate with data scientists and engineers to design and implement platform functionalities. Ensure platform quality, stability, and security.

Senior Applied AI/ML Engineer will select and adapt base models for voice assistant and intelligence tasks. Develops training, fine-tuning, and evaluation pipelines to refine models. Implements compression strategies for cost-efficient inference and collaborates with performance engineers on hardware-aware model variants.

Develops end-to-end AI-based ADAS functions, including object detection, behavior prediction, and trajectory planning. Designs and optimizes humanlike driving behavior models using various learning paradigms. Implements and executes complete training, evaluation, and release cycles, ensuring data selection, scene mining, and KPI-based validation.

Integrate and prototype GenAI & ML models for the BMW Intelligent Personal Assistant. Develop new in-car intelligence features, collaborating with model engineering and UX teams. Requires a computer science or engineering degree and 5+ years of application engineering experience.

Designs data pipelines for ingestion, curation, and versioning of data. Implements training and fine-tuning builds for GenAI edge device models. Sets up experiment tracking and artifact registries. Builds evaluation suites covering KPIs such as latency, memory, hallucination, safety, and harms.

Designs end-to-end AI and data architectures for future vehicle generations, focusing on AI trends. Builds architectural concepts for diverse use cases, such as digital cockpits and driver interaction. Provides architectural guidance to business units and development teams. Drives the implementation of scalable AI software platforms using state-of-the-art AI research. Co-creates and prioritizes AI use cases, assesses feasibility and value, and translates them into system and hardware requirements. Evaluates and implements the latest AI developments.

Designs and operates knowledge graphs and graph databases to capture domain knowledge and operational data. Builds context-engineering pipelines for LLM-based agents and implements feedback loops for continuous improvement. Develops ontologies and semantic models for the automotive AI domain.

Designs, builds, and manages Kubernetes clusters on AWS EKS for autonomous driving AI/ML workloads. Provisions and orchestrates large-scale GPU clusters for distributed AI/ML training and experimentation. Implements and automates CI/CD pipelines for AI/ML workflows. Develops and maintains infrastructure components tailored to autonomous driving data.

Subject Matter Expert (SME) for Cleanliness will define cleanliness requirements for components, assemblies, and systems. Responsibilities include foundational research on materials and coatings, design for cleanability guidance, and verification of cleanliness specifications. Experience in contamination control, material compatibility, and surface treatments is required.