• Own the reference architecture for LLM/AI features (service boundaries, APIs, data contracts, trust boundaries, deployment patterns) • Convert prototypes into production-ready services with clear interfaces, type/schema discipline, and controlled dependencies • Define versioning strategy for prompts, tools, policies, and model endpoints (including rollbacks) • Design and implement LLM pipelines for natural language → structured queries for database engines with guardrails • Implement schema-first output (e.g., Pydantic models / JSON schema) and robust parsing/validation • Own non-functional requirements: latency budgets, throughput, resilience, and cost control • Build observability: structured logs, traces, metrics, and error taxonomies that make failures actionable • Translate requirements from client stakeholders and internal security / engineering teams into robust AI-enabled solutions and data integrations • Act as the technical lead for Applied AI delivery: raise engineering bar, mentor DS/engineers, and drive code quality • Document solutions and participate in knowledge transfer with internal and partner teams.

• Developing and maintaining custom software solutions • Working with different technologies, including GCP, AWS (primarily Cloud Functions) and web development • Designing, coding, implementing, and testing applications • Creating documentation and ensuring the security and integrity of software applications • Automating business processes through custom code

• Features & Integrationen unserer Webapps planen, implementieren, testen und deployen. • n8n-Workflows planen, umsetzen, überwachen, auftretende Probleme lösen. • Umsetzung von Deployment Workflows mit GitHub Actions, laufende Verbesserung unseres Setups. • Klassische Qualitätssicherung der Updates, Code-Reviews, automatisierte Tests, Monitoring/Alerting.

• Integrating with heterogeneous government databases and legacy systems • Reverse-engineering APIs and data structures with limited documentation • Designing scalable pipelines to ingest and normalize messy property datasets • Automating complex onboarding workflows across multiple customer environments • Building infrastructure that ensures reliability and performance across deployments • Translating real-world operational constraints into robust system architecture • Contribute to and enhance our core Java codebase, emphasizing maintainability and clean design • Utilize scripting (e.g., Python, Bash) and automation tools (e.g., GitHub Actions) to streamline processes and extend system functionality • Design and implement robust integrations with third-party databases, APIs, and data storage systems • Partner with remote customer teams to understand their architecture and design implementation plans that prioritize scale and reliability • Serve as a trusted technical advisor, translating complex customer needs into tailored, scalable solutions • Work with cloud-based systems (AWS) to deploy, monitor, and scale applications, ensuring high performance, reliability, and security across customer environments.