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About Me

In my role as a leader in engineering practice, I hold expertise in strategic thinking, system integrations, and design. My skills include tech stack selection, crafting solutions across multiple platforms and leveraging the latest technologies, ensuring industry security, managing migration processes, and offering thought leadership.

I have successfully led the delivery and advancement of specialized services, showcasing proficiency in developing bespoke solutions. My capabilities extend modernization services emphasizing cloud-native characteristics and pioneering digital edge solutions encompassing IoT, IIoT, edge computing, and blockchain technologies.

In addressing operational challenges, I have implemented solutions to detect events at the scale and respond effectively. This has proven instrumental in solving issues such as predictive maintenance, predictive quality, accurate failure diagnosis, and overall efficiency improvement.

Additionally, I bring extensive experience in creating sustainable platforms that empower businesses to implement data-driven operations. This involves strategically placing computation and data storage close to the source of data.

Knowledge Areas

System Design

Architecture patterns, distributed systems, scalability, and resilience strategies.

Platform Engineering

Internal developer platforms, infrastructure automation, and delivery excellence.

Digital Strategy

Strategic frameworks for digital transformation, business model innovation, and competitive advantage.

Complete Guide to Digital Transformation

Reference Architectures

Frameworks & Platforms

Security & Integration

Enterprise Application Security Framework Enterprise API Integration and Management Platform

Optimization & Performance

Cloud Cost Optimization Framework AI Code Assistant Performance Scorecard Enterprise Test Automation Platform

Agentic AI Solutions

Sales Meeting Preparation Agent Wall Paint Visualizer Agent AI-Powered Code Modernization Kubernetes AI Agent AI-First Software Engineering Maturity Assessment

Books

AI Engineering Books

Phase 1: Foundational Knowledge

1.1 Designing Data-Intensive Applications (2nd Edition)

Build solid distributed systems and data architecture foundations essential for scalable AI infrastructure

1.2 Designing Machine Learning Systems

Learn broad ML systems knowledge covering complete ML lifecycle and production considerations before diving into LLMs

Phase 2: AI Engineering Foundations

2.1 AI Engineering - Building Applications with Foundation Models

Bridge general ML and foundation models/LLMs with modern AI engineering practices and architecture patterns

Phase 3: LLM Fundamentals & Theory

3.1 Build a Large Language Model

Gain deep understanding of how LLMs work from scratch - critical theoretical foundation before engineering applications

3.2 Hands-on Large Language Models

Apply practical, visual approach to understanding LLMs that complements theoretical knowledge

Phase 4: LLM Engineering & Production

4.1 LLM Engineer's Handbook

Master comprehensive engineering approach from concept to production with systematic methodology

4.2 Building LLMs for Production

Focus specifically on prompting, fine-tuning, and RAG with production reliability and scalability techniques

4.3 LLMs in Production

Learn production deployment, monitoring, and operational aspects of LLM systems in real-world environments

Phase 5: Specialized Techniques

5.1 Prompt Engineering for LLMs

Master the art of effective prompting - essential skill for all LLM applications

Phase 6: Applications & Agents

6.1 Building LLM Powered Applications

Learn practical application development and bridge from engineering to real-world apps

6.2 Building AI Agents with LLMs, RAG, and Knowledge Graphs

Implement specific agent architectures combining LLMs with knowledge systems

6.3 Building Agentic AI Systems

Explore advanced autonomous agent concepts including reasoning, planning, and adaptive systems

Certifications