• Build and deliver AI-powered features based on functional and technical requirements provided by TPMs and Principal AI Engineers • Develop end-to-end AI solutions, including backend services, APIs, and application-layer integrations • Integrate large language models (LLMs) into production systems, ensuring performance, scalability, and reliability • Design, test, and optimize prompts to improve output quality and consistency • Collaborate with ML Engineers to embed models trained or fine-tuned on proprietary data into applications • Participate in model evaluation and validation, ensuring outputs meet quality, accuracy, and performance benchmarks • Support testing and deployment of AI features through CI/CD pipelines • Troubleshoot and refine AI behaviours in real-world production scenarios.

• Technical implementation of software solutions with a strong focus on Artificial Intelligence • Object-oriented software development primarily in the Java ecosystem, supplemented by Python for implementing AI use cases • Design and implementation of solutions in the area of LLMs and Generative AI, especially applying prompt engineering and RAG architectures • Integration of frameworks such as LangChain or comparable technologies to optimize processes • Processing and structuring of unstructured data sets • Design and integration of interfaces (REST, OpenAPI) and connection of various services • Work in modern, container-based cloud environments (Docker, Kubernetes / OpenShift) • Ensuring the quality, traceability and performance of AI outputs

• Build AI applications: chatbots, knowledge assistants, and copilots • Implement multi-LLM orchestration across Azure OpenAI, OpenAI, and other providers • Work with data engineers to leverage datasets for RAG workflows • Integrate AI solutions into business processes using Power Platform • Deploy AI apps on Azure (Functions, App Services, AKS, VMs) • Implement CI/CD and MLOps pipelines using Azure DevOps or GitHub Actions • Monitor performance, reliability, and cost using Azure Monitor and Application Insights • Maintain technical documentation and reusable assets • Stay current with Azure AI, Copilot, and related technologies

• Design multi-agent systems with Subagents/Handoffs/Router patterns • Implement agent logic using langchain/langgraph • Design comprehensive evaluation frameworks • Optimize prompts with A/B testing • Implement state management (short-term and long-term memory) • Design RAG patterns with vector store integration • Guide customers on agent deployment and configuration management • Integrate agents into CI/CD pipelines • Collaborate with Solution Architects on infrastructure requirements • Set up observability using LangSmith • Lead agent engineering maturity assessments • Work directly with enterprise customers to understand requirements and present recommendations • Partner with Solution Architects, Engagement Managers, and Product/Engineering teams