Evidence over certificates
Every learning phase ends in working software, tests, architecture decisions, or measurable evaluation results.
14 Sep 2026 → 13 Sep 2027
365 days to
₹50 LPA.
An evidence-driven transition from mobile tech lead to senior full-stack AI engineer—through Python, FastAPI, Azure AI, RAG, agents, React, and on-device AI.
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Calendar days
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Weekly checkpoints
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Production releases
0–9h
Focused each week
Core technology map
One stack. End-to-end ownership.
The journey connects production Python and FastAPI services with PostgreSQL, Azure operations, containerized delivery, and React experiences.
The operating system
Not another course checklist.
The goal is to become credible at designing, shipping, evaluating, and operating AI products—not simply to collect tool names.
Local first, cloud when needed
Use on-device and local models for privacy, speed, and cost—then Azure for scale, managed identity, search, and operations.
Market-calibrated learning
Review real openings throughout the year and adjust the plan when employers consistently ask for different evidence.
52-week roadmap
Refresh. Build. Ship. Prove.
Each phase starts with targeted refreshers and quickly moves into production-oriented implementation.
Python engineering
Weeks 1–8 · Typed, tested, asynchronous and production-oriented Python.
Backend systems
Weeks 9–15 · FastAPI, PostgreSQL, auth, Redis, Docker and testing.
Azure foundations
Weeks 16–20 · Identity, deployment, observability and CI/CD.
LLM applications
Weeks 21–28 · Model APIs, embeddings, search, RAG and evaluation.
Agents and MCP
Weeks 29–35 · Tools, workflows, persistence and multi-agent patterns.
Full-stack AI
Weeks 36–44 · React, React Native, multimodal AI, safety and reliability.
Production release
Weeks 45–48 · Architecture, integration, evaluation and deployment.
Market readiness
Weeks 49–52 · System design, interviews, case studies and applications.
Portfolio evidence
Four releases, increasing depth.
Every release must be usable, documented, tested, and explainable in an architecture or interview conversation.
Production Python system
A maintainable, typed and fully tested Python application that establishes backend engineering depth.
Python → typing → testing → async → architecture
View build planSecure FastAPI service
Database-backed APIs with authentication, async processing, containers, CI/CD and operational telemetry.
FastAPI → PostgreSQL → auth → Docker → Azure
View build planEvaluated RAG product
Hybrid retrieval, citations, reranking, groundedness tests, failure analysis and observable quality metrics.
LLMs → embeddings → search → RAG → evaluation
View build planLocal-first agentic product
A polished React or React Native experience combining on-device intelligence with secure Azure-backed capabilities.
Agents → MCP → mobile UI → safety → production
View build planDefinition of progress
The proof will be public.
Weekly notes will show what was built, the decisions behind it, evaluation results, failures, corrections, source code, and working demonstrations. Market feedback will shape the roadmap throughout the year.
Common questions
Direct answers about the journey.
The essential facts for anyone deciding whether to follow or attempt the roadmap.
What is the 365 Days to ₹50 LPA journey?
It is a public, evidence-driven transition from mobile technical leadership to senior full-stack AI engineering. The compensation figure is a target, not a guarantee; the concrete outcome is stronger, reviewable engineering evidence.
What will be learned during the 365 days?
The roadmap covers production Python, FastAPI, PostgreSQL, authentication, Docker, Azure, LLM APIs, embeddings, search, RAG evaluation, agents, MCP, React, React Native, on-device AI, observability, security, and system design.
How much time does the journey require?
The plan uses 90-minute learning sessions from Monday to Thursday, a two-hour build session on Friday, and weekend review and publishing—for roughly eight to nine focused hours each week.
What portfolio projects will be built?
Four increasingly deep releases are planned: a production Python system, a secure FastAPI service, an evaluated RAG product, and a local-first agentic product with a React or React Native experience.
Can most of the AI work run locally?
Yes. The approach is local-first for privacy, speed, and cost. Azure is introduced where managed identity, search, deployment, scale, or production operations provide clear value.
Where can I follow each day’s work?
Every schedule entry opens a dated day page. After a session, that page is populated from a handcrafted Markdown note containing what was learned, built, tested, and corrected.