• Build a centralized reliability command center that gives teams a unified view of system health, risk, and reliability across services and environments. • Create AI agents that can assist with incident triage, root-cause exploration, log/trace summarization, and recommended next actions. • Create delightful developer-facing features and APIs that help engineers explore data, debug issues, and make better decisions. • Use AI-assisted development tools (e.g., Cursor, Claude, Copilot-like tools) as leverage to prototype, refactor, and ship high-quality code quickly. • Own projects end-to-end: requirements, architecture, implementation, testing, rollout, and iteration based on feedback. • Collaborate closely with partner teams (product, infra, data, SRE) to understand pain points and translate them into simple, powerful solutions.

• Contribute to migrating existing systems to a Kubernetes-based private cloud • Re-engineer existing systems toward modern architectures • Work closely with the software architect to implement functional and technical requirements cleanly • Develop high-performance backend solutions in the Java ecosystem • Integrate messaging systems and databases • Not just ad-hoc feature work, but structured refactoring and migration during live operation with a clear target vision

• Design, build, and deploy AI solutions hands-on using Azure AI, Azure OpenAI, and Microsoft Fabric, taking use cases from prototype to production • Own the end-to-end architecture of the Microsoft AI ecosystem, including M365 Copilot, Agent 365, and Power Platform • Develop and manage the enterprise data and analytics platform (Microsoft Fabric) to support AI, RAG, and BI use cases • Build and integrate edge-to-cloud data pipelines for OT and MES signals (e.g. Ignition, AVEVA PI) using Azure IoT and Fabric • Drive AI innovation across customer and commercial processes via Microsoft Dynamics, aligned with broader enterprise architecture • Embed security, identity (Entra ID), privacy, and responsible AI controls into all solutions • Lead and mentor a team of engineers and data scientists, reviewing designs and ensuring delivery excellence • Manage MLOps, technical debt, and lifecycle to ensure scalable, maintainable, and cost-efficient AI solutions

• End-to-end product development • Write clean, reusable, and maintainable code • Write tests to validate the correctness of your code thoroughly • Actively contribute to the identification of the problems and their solutions • Collaborate in a cross-functional team to ensure the project meets business objectives and compliance standards