AI Engineer

ZEISS Group

. Analyze use cases, data sources, data models, and integration requirements to design scalable, secure, and production-ready architectures .

Posted 10/22/2026full-timeBudapest • HungaryMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable AI architectures, integrating diverse data sources, and applying MLOps practices. Proficient in Python development and familiar with cloud-native environments, ensuring reliable deployment of agentic AI solutions.

Highest-signal resume keywords

Python DevelopmentData EngineeringMLOps PracticesMicrosoft AzureOpenAI SDK

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Hard Skills

Data PreparationData TransformationData Pipeline DesignIntegration of Data SourcesAI System DevelopmentVersioningRegression TestingSemantic SearchLogging and TracingPerformance Analysis

Soft Skills

Strong Communication SkillsCustomer FocusTeam CollaborationSelf-InitiativeFlexibility

Tools & Technologies

LangChainMicrosoft Agent FrameworkSemantic KernelLangfuseOpenTelemetryKubernetesCloud-Native ArchitecturesContainers

Industry Keywords

AI SolutionsEnterprise EnvironmentsCloud EnvironmentsHybrid-CloudOn-Premise

Tech Stack

Tools & technologies

AzureCloudKubernetesPython

About the role

Key responsibilities & impact

Analyze use cases, data sources, data models, and integration requirements to design scalable, secure, and production-ready architectures
Develop, deploy, and sustainably establish Agentic AI systems in enterprise environments, including cloud, hybrid-cloud, and on-premise environments
Evaluate technologies, frameworks, and SDKs such as OpenAI SDK, LangChain, Microsoft Agent Framework, and Semantic Kernel
Build reusable AI, agent, and data components and solutions
Integrate structured and unstructured data sources into AI, agent, and data platforms
Design and implement data pipelines, data preparation, indexing, chunking, embeddings, and semantic search
Implement logging, tracing, metrics, alerting, and performance analyses using technologies such as Langfuse and OpenTelemetry
Apply LLMOps and MLOps practices, including CI/CD, versioning, automated evaluation, regression testing, and controlled rollout/rollback strategies
Collaborate with business departments, solution designers, IT operations, and security
Support stakeholders with complex technical questions, technical concepts, prototypical implementations, and integrations
Ensure solutions are evaluated, developed as reusable components, and deployed reliably and sustainably in enterprise environments

Requirements

What you’ll need

University degree, preferably Computer Science, Business Informatics, Mathematics, Data Science, Industrial Engineering, or a comparable field of study
Several years of practical experience in implementing complex software, AI, or data projects
Knowledge of Python development and agent frameworks/SDKs such as OpenAI SDK, LangChain, Microsoft Agent Framework, Semantic Kernel, or comparable technologies
Very good knowledge of data engineering, particularly in connecting, preparing, transforming, and serving various data sources
Experience in Microsoft Azure, Kubernetes, containers, and cloud-native architectures
Experience with Langfuse, OpenTelemetry, or comparable observability technologies
Strong customer and product focus with an emphasis on business value, user adoption, reusability, and continuous improvement of productive agentic AI solutions
Confident appearance, strong communication skills, and enthusiasm
High degree of self-initiative, ability to work in a team, and flexibility
Good written and spoken German and English skills

Benefits

Comp & perks

Hybrid work arrangement