I build AI-powered systems that don’t just work in demos — they hold up in production. With 5+ years of experience across Johnson Controls and Capgemini, I’ve worked at the intersection of software engineering, distributed systems, and applied AI. Today, my focus is on AI engineering — designing workflows, agents, and systems that integrate reliably with real-world infrastructure. My core stack includes: Java, Python, C# Real-time systems with Apache Kafka Data processing using Apache Spark Cloud-native architecture with Azure Functions and Azure Kubernetes Service Workflow orchestration via Argo What differentiates my work: I don’t just “use AI” — I design systems around its failure modes I focus on behavioral correctness, not just functional correctness I’ve improved performance by 15%+ and reduced costs by ~10% through architecture decisions I build testable, production-ready systems (80%+ coverage, strong observability mindset) Most teams underestimate this: AI doesn’t break systems loudly. It introduces silent, hard-to-detect failures. That’s the gap I help close. Let’s work together If you’re: Building AI-driven features or agent workflows Moving from POC → production with AI Designing scalable, event-driven systems Or want a critical review of your architecture Let’s connect. 👉 Book a call — you’ll get clear, practical feedback (not generic advice) on how to make your system more robust, scalable, and production-ready.