Generative AI Developer at ELITS developing AI Gateway and services with modern APIs. Collaborating with cross - functional teams on cloud - based AI solutions.
About the role
- Lead AI Engineer at ZEISS developing agent-based applications and AI solutions. Collaborate with interdisciplinary teams to showcase AI innovations and ensure compliance with organizational standards.
Responsibilities
- Select and integrate AI frameworks, models, and platforms that meet the organization's requirements
- Develop agent-based AI prototypes (Agentic AI) using cloud and on-premise APIs
- Collaborate with business units to understand requirements and translate them into technical concepts
- Work closely with Enterprise Architecture Management to ensure AI solutions can be integrated into the overall enterprise architecture
- Support the handover of successful prototypes to IT and development teams for further development and scaling
- Actively monitor and engage with current AI trends and developments (Trend Radar)
- Document and communicate pilot project outcomes using KPIs such as time savings, process improvements, and innovation potential
Requirements
- University degree in Computer Science, Engineering, or a related field
- At least 5 years of relevant professional experience in developing and integrating software solutions, including at least 2 years working with modern AI systems (e.g., Generative AI, LLMs, Agentic AI)
- Demonstrable ability to evaluate AI technologies in a business context and showcase their potential
- Strong knowledge of Python for AI development and an understanding of .NET/C# interfaces for system integration
- Experience with agentic frameworks and orchestration tools such as LangChain (LangGraph), Microsoft Semantic Kernel, or AutoGen
- Proficiency working with LLMs, both cloud-based (e.g., Azure OpenAI) and on-premise/local (e.g., vLLM, Ollama, Hugging Face)
- Practical experience with RAG architectures (Retrieval-Augmented Generation) and vector databases (e.g., Qdrant, Azure AI Search)
- Familiarity with API development (e.g., FastAPI) and containerization (Docker) for delivering functional prototypes
Benefits
- Support teams in adopting AI technologies, including creating roadmaps and actionable recommendations
- Conduct workshops, trainings, and educational sessions to foster acceptance and understanding of AI technologies
- Build an internal AI community to strengthen knowledge sharing and collaboration
Job title
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