Generative AI & Agentic AI Solutions Developer (f/m/div.)

As a Generative AI & Agentic AI Solutions Developer, you will help design and implement innovative AI-powered solutions that create measurable value across engineering, manufacturing and business operations. You will work closely with domain experts, software engineers and business stakeholders to transform complex challenges into scalable AI solutions.
Your contribution to something big:
Identify and prioritize opportunities where Generative AI and Agentic AI can drive business impact and operational excellence.
Design, develop and validate AI-powered solutions, including AI copilots, Retrieval-Augmented Generation (RAG) applications, AI agents and agentic workflows.
Translate business requirements and knowledge-intensive processes into scalable AI-assisted solutions.
Integrate AI solutions with enterprise platforms, business applications and data ecosystems.
Collaborate with cross-functional and international teams to deliver production-ready AI applications.
Contribute to the standardization, reuse and continuous improvement of AI assets, frameworks and best practices.
Support the adoption of responsible, secure and scalable AI technologies across the organization.
Stay up to date with emerging AI technologies and help shape Bosch's AI innovation journey.
What distinguishes you:
Education
MSc in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Mathematics, Physics, Engineering or a related field;
Or equivalent practical experience in software development and AI solution implementation.
Experience
Experience developing Generative AI applications in enterprise environments.
Experience implementing AI-powered solutions from concept through deployment.
Experience working with cross-functional and multidisciplinary teams.
Ability to connect technical solutions with measurable business outcomes.
Experience integrating AI solutions into existing systems and business processes.
Experience in manufacturing, industrial engineering or technology-driven environments is considered an advantage.
Know-how
1. Generative AI & Large Language Models (LLMs)
Understanding of Generative AI concepts, foundation models and Large Language Models (LLMs).
Familiarity with platforms and providers such as OpenAI, Azure OpenAI, Claude, Gemini, Llama or similar technologies.
Experience designing prompts, grounding strategies and context-management approaches for reliable AI outputs.
2. Retrieval-Augmented Generation (RAG) & Knowledge Systems
Knowledge of Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search and information retrieval techniques.
Experience working with enterprise knowledge sources, document repositories and structured or unstructured data.
Understanding of knowledge management principles and trustworthy AI responses.
3. Agentic AI & Workflow Orchestration
Knowledge of AI agents, agentic workflows, multi-step reasoning and human-in-the-loop approaches.
Familiarity with tool calling, function calling, MCP (Model Context Protocol) and workflow orchestration concepts.
Experience with frameworks such as LangChain, LangGraph, LlamaIndex or comparable technologies.
4. Software Development
Solid Python programming skills.
Knowledge of API development, API integration and object-oriented programming principles.
Experience with Git, testing, debugging and maintainable software development practices.
5. Cloud & Enterprise Integration
Familiarity with Azure AI services, cloud platforms, deployment architectures and containerization.
Understanding of authentication, authorization and enterprise application integration.
Knowledge of model selection, token management, performance optimization and cost-efficient AI deployments.
6. AI Evaluation, Safety & Governance
Understanding of evaluation frameworks, benchmarking methods and quality metrics for Generative AI applications.
Knowledge of hallucination mitigation, monitoring, observability and AI quality assurance practices.
Awareness of Responsible AI, security, compliance and governance requirements in enterprise environments.
Languages
English proficiency, both written and spoken.
Working Style and Methods
Structured, analytical and solution-oriented approach.
Proactive, self-driven and accountable mindset.
Strong collaboration and communication skills.
Pragmatic attitude focused on business value and user outcomes.
Agile and iterative way of working with a passion for continuous learning and improvement.
Personality
Curious and innovation-driven.
Passionate about emerging AI technologies and their practical applications.
Open-minded, collaborative and eager to share knowledge.
Comfortable working in dynamic and multidisciplinary environments.

Employment type
Full-time

Work arrangement
Hybrid