Role Description We're looking for a motivated Engineering student who is eager to build real software products, learn from experienced engineers, and contribute to innovative solutions. If you're passionate about software development, AI, and solving challenging problems, we'd love to hear from you. - Develop new features for our software products. - Build modern web applications using React and TypeScript. - Leverage AI-powered development tools to accelerate software development. - Work with cloud-native technologies, including Microsoft Azure and Kubernetes. - Collaborate with experienced software engineers throughout the entire development lifecycle. - Propose and implement new ideas and product features that can have a real impact. Qualifications - Currently pursuing a Bachelor's or Master's degree in: - Computer Engineering - Electrical and Computer Engineering - Computer Science - or a related field. - Passion for software engineering and emerging technologies. - Curiosity, initiative, and a willingness to learn. - Strong problem-solving skills. - Ability to work collaboratively in a team environment. - Familiarity with React or TypeScript is a plus, but not required. Benefits - A hands-on internship working on real products, not sample projects. - The opportunity to contribute ideas and influence product development. - Continuous mentoring and technical guidance from experienced engineers. - Training throughout the internship. - Exposure to modern Cloud and AI technologies. - A collaborative and innovative work environment. - Standard working hours. - Internship duration of 2 months. Company Description We are a dynamic IT company specializing in consulting and implementation services for our clients. Our current focus lies in cloud implementation and management, covering areas such as cloud architecture and infrastructure, cloud migration, IT managed services, and software development. Many renowned companies already rely on our expertise and trust in the quality of our work. Our story began in 2014 with the founding of XIBIX Solutions GmbH, an IT startup based in Unterschleißheim, just north of Munich. Since then, we’ve experienced strong growth — and we’re far from done! With ambitious plans ahead, we’re looking for motivated talents across all areas to join the XIBIX IT Group at our locations in Hamburg, Munich, Constance, Landshut, and Porto or remotely. At XIBIX, we bring together doers, creatives, and experts who inspire our clients with innovative solutions and a genuine curiosity to explore new ideas. Driven by our passion for cloud technologies, we empower national and international companies to become fit for the digital future.

• Build Navanta’s retrieval and verifications over data systems, with shown queries and citations for every answer • Stand up self-hosted open-weight models serving and embeddings inside each bank’s environment or shared environments for Navanta; evolve RAG to a dedicated standard • Design the MCP tool layer that exposes a small, audited set of read-only tools (metrics, documents, customer 360), eventually growing into read/write tools with heavy amounts of regulated, highly sensitive data • Build and maintain the evaluation harness — golden-question regression, groundedness and retrieval metrics, explicit “I don’t know” behavior — and make it a release gate • Implement LLM guardrails: PII redaction in prompts and context, prompt-injection defenses, and cost and row limits aligned to regulatory security expectations • Partner with data teams so the model selects governed metrics from the semantic layer rather than improvising SQL • Document model architecture, evaluation methodology, and guardrail controls to support customer security reviews and audit readiness • Track latency, cost, and quality trade-offs across model versions and deployment configurations

• Design and implement Generative AI models customized to meet project-specific requirements. • Collaborate with cross-functional teams to integrate advanced AI solutions using Python .NET and API integration methodologies. • Develop and deliver enterprise-grade AI solutions, leveraging expertise in Generative AI, modern application development, and cloud-native engineering practices. • Deploy and manage scalable AI solutions on Azure platforms to ensure performance and reliability. • Integrate OpenAI technologies and RAG methodologies into existing software systems for enhanced functionality. • Contribute actively to Agile workflows, including sprint planning, reviews, and retrospectives. • Maintain code quality and manage version control effectively using Git. • Communicate technical progress, challenges, and goals effectively with team members and stakeholders.

• Design, build, and maintain agentic systems and LLM-powered applications that automate healthcare workflows, data pipelines, and clinical decision support — from conception through production deployment • Build and orchestrate agents using LLM APIs (OpenAI, Anthropic, etc.) and agentic frameworks (LangChain, LangGraph, CrewAI, or custom orchestration) to solve complex, multi-step healthcare problems • Develop prompt libraries, agent instructions, and reusable "skills" that improve agent accuracy, consistency, and reliability across different use cases and data domains • Build validation and confidence-scoring layers that flag low-confidence agent decisions for human review before production deployment; establish guardrails and review workflows for agent-authored code and outputs • Own end-to-end delivery of AI-automated systems — from problem scoping and requirements gathering through agent development, testing, and validated production deployment • Implement rigorous evaluation and QA frameworks for agentic systems — including golden datasets, test cases, output validation, hallucination detection, and regression testing • Establish and maintain evaluation metrics for agent performance, reliability, and clinical appropriateness; measure agent accuracy, hallucination rates, clinical validity, and real-world impact • Implement observability, evaluation, and regression testing frameworks specific to agentic systems — decision tracing, lineage logging, and performance tracking • Collaborate with data engineering and platform teams to integrate agent-built outputs (dbt models, transformation logic, recommendations) into existing data architectures and clinical workflows • Ensure all agentic systems comply with healthcare regulations (HIPAA, FDA guidance on AI/ML) and responsible AI practices — including explainability, auditability, and clinician trust • Continuously evaluate new LLM models, agent frameworks, prompt engineering techniques, and tooling; recommend adoption or migration based on healthcare-specific requirements (accuracy, cost, latency, regulatory alignment) • Partner with data engineering to establish robust data validation and input validation layers for agents — agents are only as good as the data they operate on • Lead experimentation and measurement of AI-automated systems impact on speed, quality, compliance, and cost across healthcare workflows • Document agent architectures, prompt strategies, evaluation frameworks, and best practices for both technical and non-technical stakeholders • Mentor AI Connector Engineers and other team members on agentic development patterns, LLM-powered application design, and responsible AI practices • Work on-call as needed to support production agentic systems, troubleshoot agent issues, and respond to performance degradation or hallucination detection