About Mihir Jha¶

I’m a Software Architect with 14+ years of experience designing and building scalable, high-performance, secure, and cloud-native systems across finance, banking, and telecom.

My professional background spans:

• Distributed Systems
• Microservices
• Event-Driven Architecture
• Cloud-Native Engineering
• AWS, Azure & GCP
• Java, Kotlin & Spring
• Kubernetes & Terraform
• Production System Design

My engineering background is rooted in building reliable backend and distributed systems, and my current journey is focused on extending that foundation into AI Engineering and Enterprise AI Architecture.

🎯 Current Focus¶

I’m focused on the intersection of:

Software Engineering + Cloud Architecture + AI Engineering

My current areas of exploration include:

• Machine Learning & Deep Learning
• Foundation Models & Large Language Models
• Generative AI
• Retrieval-Augmented Generation (RAG)
• Advanced Retrieval
• AI Agents & Agentic AI
• Multi-Agent Systems
• AI System Design
• Enterprise AI Architecture
• MLOps & LLMOps
• Cloud AI Engineering
• AI-powered Microservices
• AI Infrastructure & Platform Engineering

The broader objective is to understand how modern AI capabilities can be integrated into scalable, secure, observable, and production-ready software systems.

🧠 My Engineering Perspective¶

I approach AI from an engineering and architecture perspective.

For me, learning an AI technology is not only about understanding:

How does it work?

It is also about understanding:

How does it fit into a system?
        ↓
How does it scale?
        ↓
How does it fail?
        ↓
How do we secure it?
        ↓
How do we observe and evaluate it?
        ↓
How do we control cost?
        ↓
How do we operate it reliably?

This perspective connects my background in backend and cloud engineering with modern AI systems.

📚 About This Blog¶

The Enterprise AI Engineering Blog is where I publish technical articles, architecture deep dives, engineering perspectives, implementation insights, and practical lessons from exploring modern software and AI systems.

The focus is on understanding not only how technologies work, but also:

• How they fit into production architectures
• What trade-offs they introduce
• How they scale
• How they should be secured
• How they should be observed and evaluated
• How they integrate with existing backend systems
• How cloud infrastructure influences architecture
• How AI capabilities become production software

The blog is organized around three complementary content journeys.

🤖 AI for Backend Engineers¶

Learn AI

A practical learning journey connecting:

• Machine Learning
• Deep Learning
• Foundation Models
• Large Language Models
• Prompt Engineering
• RAG
• AI Agents
• Cloud AI
• Production AI Engineering

The goal is to help backend engineers understand how AI capabilities can become part of modern software systems.

🧠 Inside Modern AI Systems¶

Understand AI

A hands-on exploration of the internal components behind modern AI systems.

The series focuses on:

• Neural Networks
• Transformers
• LLM Internals
• AI Training
• AI Inference
• PyTorch
• TensorFlow / Keras
• Performance and optimization

The goal is to move beyond framework abstractions and understand what is happening underneath them.

🏢 Enterprise AI Engineering¶

Architect AI

A production-focused architecture journey exploring how enterprise-grade AI systems are designed and operated.

The broader focus includes:

• Enterprise AI Architecture
• RAG Systems
• AI Gateways
• Agentic AI
• AI Platforms
• AI Observability
• LLMOps
• AI Security
• AI Governance
• AI Inference Infrastructure
• Distributed AI Systems

The goal is to understand how individual AI capabilities become scalable enterprise systems.

🧭 The Overall Journey¶

The three content tracks complement one another:

flowchart LR
    A[AI for Backend Engineers]
    B[Inside Modern AI Systems]
    C[Enterprise AI Engineering]

    A --> A1[Learn AI]
    B --> B1[Understand AI Internals]
    C --> C1[Architect AI Systems]

    A1 --> D[AI Engineering]
    B1 --> D
    C1 --> D

The broader progression is:

Learn
  ↓
Understand
  ↓
Build
  ↓
Architect
  ↓
Operate
  ↓
Optimize

📖 Enterprise AI Engineering Handbook¶

For structured, chapter-based technical learning and reference material, visit the:

Enterprise AI Engineering Handbook

The blog and handbook serve different purposes.

Blog
  ↓
Articles, Deep Dives & Engineering Perspectives

Handbook
  ↓
Structured Technical Reference

GitHub
  ↓
Code, Projects & Experiments

LinkedIn
  ↓
Discussion, Distribution & Community

Newsletter
  ↓
Ongoing Enterprise AI Engineering Updates

💻 GitHub¶

I use GitHub to support the learning and engineering journey with:

• Source code
• AI experiments
• Architecture examples
• Implementation projects
• Notebooks
• Supporting documentation

GitHub — MihirKJha

💼 LinkedIn¶

I regularly share:

• Technical insights
• Architecture discussions
• Compact versions of articles
• Engineering perspectives
• New article announcements
• Enterprise AI discussions

LinkedIn — Mihir Jha

📰 Enterprise AI Engineering Newsletter¶

The Enterprise AI Engineering newsletter focuses on practical, production-oriented perspectives across:

• Enterprise AI Engineering
• AI System Design
• Cloud Architecture
• Backend Engineering
• Generative AI & LLMs
• RAG & Advanced Retrieval
• AI Agents & Agentic AI
• MLOps & LLMOps
• Production AI Architecture

Read the Enterprise AI Engineering Newsletter

🎯 My Goal¶

My goal is to bridge traditional software and cloud engineering with modern AI engineering and document practical approaches for designing:

• Scalable systems
• Secure systems
• Observable systems
• Reliable systems
• Cost-aware systems
• Production-ready intelligent systems

I want this blog, the handbook, and the supporting projects to become a practical record of that journey.

🚀 The Long-Term Direction¶

The broader journey is moving from:

Software Engineering
        ↓
Cloud Engineering
        ↓
AI Engineering
        ↓
AI Systems Engineering
        ↓
Enterprise AI Architecture

The long-term objective is to understand how to build intelligent systems that are not only technically capable, but also reliable, maintainable, secure, observable, and scalable.

👨‍💻 About Me¶

Mihir Jha

Software Architect | AI Engineering | Cloud Architecture | Backend Engineering

Focused on bridging traditional software and cloud engineering with modern AI engineering to design scalable, secure, observable, and production-ready intelligent systems.