I design and build AI systems that go beyond automation—systems that analyze, reason, and adapt. With 18+ years of experience in software engineering, cloud infrastructure, and AI, I focus on developing intelligent architectures that integrate large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent systems for enterprise applications.
Right now, I’m working on:
- AI system architecture – Designing modular, scalable AI infrastructures
- Multi-agent systems – Building autonomous agents that collaborate for data analysis and security
- Retrieval-augmented generation (RAG) – Improving how AI retrieves and processes information
- Event-driven architectures – Creating scalable pipelines for AI-powered data workflows
- Autonomous Agents – Multi-agent architectures, cognitive AI models
- RAG & LLMs – Hybrid retrieval, semantic search, knowledge augmentation
- MLOps – Model deployment, monitoring, and optimization
- Cloud Platforms – AWS, Azure, cloud-native architectures
- Data & Event Processing – Kafka, RabbitMQ, real-time data pipelines
- DevOps & Automation – Terraform, CI/CD, infrastructure-as-code
- Frontend – React, Vue.js, component-driven architectures
- Backend – Node.js, FastAPI, high-performance APIs
- Data & APIs – REST, GraphQL, gRPC, structured data flow
I’ve designed AI-driven platforms for security, automation, and enterprise intelligence, including:
- Enterprise AI Agents – Multi-agent systems for automated data analysis and cybersecurity
- Intelligent Knowledge Retrieval – RAG-powered systems that improve AI-driven search and decision-making
- Cognitive AI Frameworks – Architectures that enable AI agents to work together efficiently
- Real-Time Data Processing – Scalable AI pipelines for analyzing large datasets on the fly
I enjoy discussing AI system design, scalable architectures, and how to make AI work in real-world enterprise environments.
LinkedIn | Email: [email protected]
AI should be more than just automation—it should think, adapt, and solve real problems.