Santosh Vaza svaza

🗺️ Roadmap: Principal Engineer to Azure Solution Architect (2026-2027)

Current Role: Principal Engineer (App/Dev Focus)
Target Role: Enterprise/Platform Architect (Infrastructure, Governance & Strategy Focus)
Core Philosophy: "Mechanics over Magic." Shift focus from building code to governing the ecosystem.

📚 Phase 0: Foundations & The "Architect's Mindset"

This runs continuously alongside the technical phases.

6-Month Roadmap — Expert in RAG & Enterprise LLM Solutions (Azure + on‑prem)

https://www.louisbouchard.ai/from-zero-to-hero-with-llms/

Goal: Design, build, secure, and operate production-grade RAG systems integrating enterprise data, using vector DBs, and applying adapters as needed.

Month 0 — Foundations (1–2 weeks)

Outcome: Understand LLM fundamentals, embeddings, chunking, RAG architecture, and prompt basics.

🎯 Minimalist AI/ML Learning Plan for Principal Software Engineers & Architects

This curated list focuses on essential AI/ML knowledge to help you architect, lead, and guide AI/ML initiatives, without going deep into model-building.

🧠 1. Understand AI at a Strategic Level

✅ AI for Everyone – Andrew Ng (Coursera)

Platform: Coursera

🎯 Azure Architect Expert Roadmap (Enhanced with Relevant Courses)

Objective

Become proficient in designing, implementing, and managing solutions on Microsoft Azure, focusing on both enterprise architecture and cloud infrastructure.

Prerequisites:

Strong background in cloud computing concepts.
Experience in software development, especially with .NET technologies.
Knowledge of networking, security, and storage concepts.

.NET Solution Architect - In-Depth Learning Roadmap

Phase 1: Core Technical Expertise

1. .NET Fundamentals & Advanced Concepts

✅ C# 12, LINQ, Async Programming, Memory Management
✅ .NET Core & .NET 8 (ASP.NET Core, API development, Dependency Injection)
✅ Clean Code, SOLID Principles, Design Patterns

📚 Courses:

ML Starter Roadmap for a .NET & Azure Developer

🎯 Goal:

To understand core ML concepts, build simple models, and integrate them with .NET & Azure.

Phase 1: ML Fundamentals Without the Overhead (Month 1)

1️⃣ Basics of ML & AI

Machine Learning Specialization by Andrew Ng (Course 1: Supervised ML)

🎯 Generative AI Roadmap for Intelligent Application Development (With Azure Integration)

Objective

Leverage Generative AI and pre-trained ML models using APIs/SDKs (OpenAI, Azure, HuggingFace, etc.) to:

Build intelligent business apps
Solve real-world problems
Integrate AI features into existing backends

	public class Solution {
	public int EdgeScore(int[] edges) {
	long[] deg = new long[edges.Length];
	long max = -1;
	for(int i = 0; i < edges.Length; i++)
	{
	deg[edges[i]]+=i;
	max = Math.Max(max, deg[edges[i]]);
	}

	public class Solution {
	public int ArithmeticTriplets(int[] nums, int diff) {
	var set = new HashSet<string>();

	int i = 0;
	int n = nums.Length-1;
	int c = 0;

	while(i <= n-2)
	{

	public class Solution {
	public int LatestTimeCatchTheBus(int[] buses, int[] passengers, int capacity) {
	PriorityQueue<int, int> qbus = new();
	PriorityQueue<int, int> qpass = new();
	HashSet<int> set = new();

	foreach(var bus in buses) qbus.Enqueue(bus, bus);

	foreach(var p in passengers)
	{