6-Month AI Engineer Career Transition Roadmap
Goal: Transition from Senior Software Engineer to AI Engineer / Generative AI Engineer / LLM Engineer within 6 months by combining existing software-engineering experience with practical AI, LLM, Agentic AI, and MLOps skills.
1. Career Goal
Current Profile
- Senior Software Engineer / Team Lead
- ~3.5 years professional experience
- B.Tech in IT
- Strong backend engineering experience
- Node.js / TypeScript
- NestJS
- Microservices
- Angular
- Docker
- AWS
- CI/CD
- GitHub Actions
- REST APIs
- WebSockets
- Blockchain / EVM
- Distributed systems
Target Roles
After completing this roadmap, target roles such as:
- AI Engineer
- Generative AI Engineer
- LLM Engineer
- AI Application Engineer
- AI/ML Engineer
- AI Platform Engineer
- Backend Engineer — AI/GenAI
- Agentic AI Engineer
Target Positioning
Do not position yourself as:
Software Engineer who learned ChatGPT.
Instead, position yourself as:
Senior Software Engineer specializing in production AI/LLM systems, RAG, Agentic AI and AI infrastructure.
2. Why Your Existing Experience Is Valuable
You do not need to start your career over.
Your existing engineering experience maps very well to AI engineering.
| Existing Skill | AI Engineering Application |
|---|---|
| Node.js / NestJS | AI APIs and inference services |
| TypeScript | AI application development |
| Microservices | AI agent/service architecture |
| REST APIs | LLM/tool APIs |
| WebSockets | Streaming LLM responses |
| Databases | Vector databases / RAG storage |
| Docker | AI/ML deployment |
| AWS | AI infrastructure |
| GitHub Actions | ML/LLM CI/CD |
| System Design | AI system architecture |
| Blockchain | Specialized AI domain |
| Distributed Systems | Agent orchestration / AI platforms |
Your biggest gaps are:
Python
↓
Machine Learning
↓
Deep Learning
↓
Transformers
↓
LLMs
↓
RAG
↓
LLM Evaluation
↓
Agents
↓
MCP
↓
MLOps / AI Infrastructure
3. Six-Month Roadmap Overview
MONTH 1
Python + Machine Learning Fundamentals
↓
MONTH 2
Deep Learning + PyTorch + Transformers
↓
MONTH 3
LLM Engineering + RAG
↓
MONTH 4
Production RAG + AI Systems + Evaluation
↓
MONTH 5
Agents + LangGraph + MCP
↓
MONTH 6
MLOps + Production Deployment + Portfolio + Interviews
Core principle
30% learning + 70% building
Do not spend six months watching courses.
Every month must produce something that goes onto GitHub.
4. Recommended Udemy Courses
Course 1 — Machine Learning A-Z [2026]
Purpose: Machine learning fundamentals
Current information
- Rating: ~4.5/5
- Reviews: 200K+
- Students: 1M+
- Updated: 2026
Link
https://www.udemy.com/course/machinelearning/
Topics
- Python for ML
- Data preprocessing
- Regression
- Classification
- Decision trees
- Random forests
- SVM
- XGBoost
- Clustering
- Model evaluation
- Feature engineering
- Neural networks
- NLP
- Reinforcement learning
Priority
⭐⭐⭐⭐
Recommendation
Do not obsess over completing every lecture.
Focus on understanding:
- What ML is
- Supervised vs unsupervised learning
- Training / validation / test datasets
- Overfitting
- Underfitting
- Feature engineering
- Model evaluation
- Choosing the right algorithm
5. Month 1 — Python + Machine Learning
Goal
Become comfortable enough with Python and ML to build and deploy basic machine-learning systems.
Python topics
You should know:
Python
├── Variables
├── Functions
├── Classes
├── Decorators
├── Type hints
├── Async programming
├── Exception handling
├── Virtual environments
├── pip / package management
├── NumPy
├── Pandas
└── Jupyter
Because you already know TypeScript, you don't need an extremely long Python course.
Learn enough Python to comfortably read and write AI/ML code.
ML topics
Focus on:
Regression
Classification
Decision Trees
Random Forest
XGBoost
Clustering
Feature Engineering
Model Evaluation
Cross Validation
Overfitting
Underfitting
Month 1 Project
Customer Churn Prediction API
Architecture
Dataset
↓
Data preprocessing
↓
ML training
↓
Model evaluation
↓
Saved model
↓
FastAPI service
↓
Docker
↓
AWS
Technology
Python
Pandas
NumPy
Scikit-learn
FastAPI
Docker
AWS
Features
The API should expose something like:
POST /predict
Input:
{
"age": 35,
"tenure": 24,
"monthly_charges": 79.5,
"contract": "monthly"
}
Output:
{
"churn_probability": 0.78,
"prediction": "high_risk"
}
GitHub repository
ml-churn-prediction
Include:
- README
- Architecture diagram
- Dataset description
- Training process
- Evaluation metrics
- API documentation
- Docker setup
- Deployment instructions
6. Month 2 — Deep Learning + PyTorch
Course
PyTorch for Deep Learning Bootcamp
Current information
- Rating: ~4.6/5
- Reviews: ~6K+
- Students: ~50K+
- Updated: 2026
Link
https://www.udemy.com/course/pytorch-for-deep-learning/
Priority
⭐⭐⭐⭐
Topics
Focus on:
Tensors
↓
Datasets
↓
Neural Networks
↓
Forward Pass
↓
Loss Function
↓
Backpropagation
↓
Optimizers
↓
Training
↓
Evaluation
↓
Inference
Understand the concept:
Input
↓
Neural Network
↓
Prediction
↓
Loss
↓
Backpropagation
↓
Optimizer
↓
Updated Weights
You don't need to become a research-level deep-learning specialist.
7. Transformers Fundamentals
Understand this progression:
Traditional NLP
↓
RNN
↓
LSTM
↓
Attention
↓
Transformer
↓
BERT
↓
GPT
↓
LLMs
Important concepts:
- Tokenization
- Embeddings
- Attention
- Self-attention
- Positional encoding
- Encoder
- Decoder
- Transformer architecture
- Context window
- Parameters
Month 2 Project
Customer Support Ticket Classifier
Architecture
Customer message
↓
Preprocessing
↓
Transformer model
↓
Classification
↓
┌─────────┬──────────┬─────────┐
↓ ↓ ↓
Billing Technical Account
Technology
Python
PyTorch
Hugging Face
Transformers
FastAPI
Docker
AWS
GitHub repository
transformer-ticket-classifier
8. Month 3 — LLM Engineering
This is the most important stage of the roadmap.
Course
AI Engineer Core Track
LLM Engineering, RAG, QLoRA, Agents
Current information
- Rating: ~4.6/5
- Reviews: ~42K+
- Students: ~350K+
- Updated: 2026
Link
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models/
Priority
⭐⭐⭐⭐⭐
9. LLM Learning Sequence
Learn in this order:
LLM
↓
Tokenization
↓
Embeddings
↓
Transformers
↓
Prompting
↓
Structured Output
↓
Function Calling
↓
RAG
↓
Vector Database
↓
Fine-tuning
↓
LoRA
↓
QLoRA
↓
Agents
10. Understand When to Use What
This is an important AI-engineering skill.
| Problem | Solution |
|---|---|
| Change model behavior | Prompting |
| Give model private/current knowledge | RAG |
| Teach specialized behavior/style | Fine-tuning |
| Allow model to perform actions | Tool calling |
| Multi-step autonomous workflow | Agent |
| Connect external tools/services | MCP |
Do not blindly use agents for everything.
11. Month 3 Project — Enterprise RAG
This should become your first major portfolio project.
Architecture
Documents
│
↓
Ingestion
│
↓
Chunking
│
↓
Embeddings
│
↓
PostgreSQL + pgvector
│
↓
User ─────────→ Retrieval
│
↓
Reranking
│
↓
LLM
│
↓
Answer + Citations
Recommended stack
Python
FastAPI
PostgreSQL
pgvector
OpenAI / Anthropic
LangChain
Docker
AWS
Why PostgreSQL + pgvector?
You already understand relational databases.
There is no need to learn five vector databases immediately.
Start with:
PostgreSQL + pgvector
Later learn:
- Pinecone
- Qdrant
- Weaviate
- Milvus
conceptually.
12. Enterprise RAG Features
Your system should support:
- PDF ingestion
- DOCX ingestion
- Text extraction
- Chunking
- Embeddings
- Vector search
- Metadata filtering
- RAG
- Citations
- Conversation history
- Streaming responses
- Authentication
- Document permissions
Example:
User:
"What is our company's leave policy?"
↓
Query embedding
↓
Vector search
↓
Relevant documents
↓
LLM
↓
"According to the HR policy..."
↓
Source citations
13. Month 4 — Production AI Engineering
The goal now is to move from:
"I can build a RAG demo."
to:
"I can design and operate a production RAG system."
14. Learn Advanced RAG
Understand:
Document ingestion
↓
Chunking
↓
Embedding
↓
Vector search
↓
Hybrid search
↓
Metadata filtering
↓
Reranking
↓
Context construction
↓
LLM
↓
Evaluation
Learn:
- Chunking strategies
- Semantic chunking
- Hybrid search
- BM25
- Metadata filtering
- Reranking
- Query rewriting
- Multi-query retrieval
- Context compression
15. Learn LLM Evaluation
This is one of the most important skills separating production AI engineers from tutorial developers.
You need to answer:
"How do you know your AI system is good?"
Learn:
Retrieval precision
Retrieval recall
Answer relevance
Faithfulness
Groundedness
Hallucination detection
Latency
Token usage
Cost
Build an evaluation dataset.
Example:
Question
Expected Answer
Retrieved Documents
Generated Answer
Score
Then run evaluation automatically.
16. Month 4 Project — Production Knowledge Assistant
Upgrade your Month 3 RAG project.
Architecture
Angular
↓
FastAPI
↓
Authentication
↓
RAG Service
↓
Retriever
↓
Reranker
↓
LLM
↓
PostgreSQL + pgvector
Add
Authentication
User
↓
JWT
↓
Authorization
Document permissions
Company
├── Engineering
├── HR
└── Finance
A user should only retrieve documents they're allowed to access.
Streaming
Implement:
LLM
↓
Streaming tokens
↓
FastAPI
↓
WebSocket / SSE
↓
Angular
Your existing WebSocket experience becomes useful here.
17. Observability
Add:
Logging
Tracing
Token usage
Latency
Error tracking
Cost tracking
Evaluation metrics
Track things such as:
Request
├── Prompt tokens
├── Completion tokens
├── Total tokens
├── Latency
├── Retrieved documents
├── Model
├── Cost
└── Evaluation score
18. Month 5 — Agentic AI
Course
LangChain — Agentic AI Engineering with LangChain & LangGraph
Current information
- Rating: ~4.6/5
- Reviews: ~54K+
- Students: ~220K+
- Updated: 2026
Link
https://www.udemy.com/course/langchain/
Priority
⭐⭐⭐⭐⭐
19. Learn Agents
Understand:
LLM
↓
Tool Calling
↓
Agent
↓
State
↓
Memory
↓
Planning
↓
LangGraph
↓
Multi-Agent
↓
MCP
20. LangGraph
Focus heavily on:
- State
- Nodes
- Edges
- Conditional routing
- Checkpoints
- Human-in-the-loop
- Tool execution
- Error handling
- Agent workflows
Think of LangGraph as an orchestration layer.
Example:
START
│
↓
Planner
│
┌─────┴─────┐
↓ ↓
Research Coding
│ │
└─────┬─────┘
↓
Reviewer
│
┌─────┴─────┐
↓ ↓
Approved Rework
│ │
└─────┬─────┘
↓
END
21. MCP
Learn:
Model Context Protocol
Understand:
- MCP clients
- MCP servers
- Tools
- Resources
- Prompts
- Tool discovery
- Authentication
- Security
Don't just memorize MCP.
Understand why it exists:
A standardized way for AI applications to interact with external tools and context.
22. Optional Course — Agentic AI
AI Engineer Agentic Track
Current information
- Rating: ~4.7/5
- Reviews: ~49K+
- Students: ~400K+
- Updated: 2026
Link
https://www.udemy.com/course/the-complete-agentic-ai-engineering-course/
Priority
⭐⭐⭐⭐
Important
Do not try to master every agent framework.
You do not need:
LangChain
CrewAI
AutoGen
Semantic Kernel
LlamaIndex
Haystack
all at expert level.
Go deep on:
LangGraph
OpenAI Agents SDK
MCP
Know other frameworks conceptually.
23. Month 5 Flagship Project
AI Software Engineering Agent
This project is particularly suitable for your background.
Architecture
User
│
↓
AI Orchestrator
│
┌───────────┼───────────┐
↓ ↓ ↓
Research Coding Testing
Agent Agent Agent
│ │ │
↓ ↓ ↓
Web GitHub Docker
Search Repository Test Runner
│ │ │
└───────────┼───────────┘
↓
Reviewer
│
↓
Final Result
Give the agent tools
GitHub
Jira
Web Search
File System
Database
Docker
Terminal
Example workflow
User:
"Analyze this GitHub issue and propose a fix."
Agent:
Issue
↓
Research Agent
↓
Repository Agent
↓
Code Agent
↓
Test Agent
↓
Reviewer
↓
Final response
This demonstrates:
- LLMs
- Agents
- Tool calling
- LangGraph
- MCP
- GitHub integration
- Code execution
- Docker
- System design
24. Month 6 — MLOps + Production AI
Course
MLOps Zero to Hero
Current information
- Rating: ~4.6/5
- Reviews: ~2.5K+
- Students: ~23K+
- Updated: 2026
Link
https://www.udemy.com/course/mlops-zero-to-hero/
Priority
⭐⭐⭐⭐
25. MLOps Topics
Learn:
Experiment tracking
↓
Model versioning
↓
Model registry
↓
Model deployment
↓
Inference
↓
Monitoring
↓
Evaluation
↓
Retraining
Tools:
MLflow
DVC
Docker
Kubernetes
AWS
KServe
SageMaker
Kubeflow
Important for you
You already know:
- Docker
- AWS
- CI/CD
- GitHub Actions
- Microservices
- Cloud infrastructure
Therefore:
Do not spend weeks learning basic DevOps again.
Focus on AI-specific infrastructure.
26. Month 6 — Productionize Your Best Project
Take your best AI project and deploy it properly.
Example:
CloudFront
│
↓
Angular
│
↓
API Gateway
│
↓
FastAPI
│
┌────────┴────────┐
↓ ↓
AI Service Auth
│
┌─────┴─────┐
↓ ↓
LLM RAG
│ │
↓ ↓
Provider PostgreSQL
│
pgvector
27. Production Features
Implement:
- Docker
- CI/CD
- AWS deployment
- Authentication
- Authorization
- Rate limiting
- Caching
- Logging
- Monitoring
- Tracing
- Evaluation
- Cost tracking
- Error handling
- Retry logic
- Model fallback
- Prompt versioning
28. Final Portfolio
By the end of six months, aim for 4–5 strong projects.
Do not create 20 small ChatGPT clones.
Quality > quantity.
Project 1 — ML Churn Prediction
Repository
ml-churn-prediction
Demonstrates
- Python
- Pandas
- Scikit-learn
- ML
- Model evaluation
- FastAPI
- Docker
- AWS
Project 2 — Transformer Ticket Classifier
Repository
transformer-ticket-classifier
Demonstrates
- PyTorch
- Transformers
- Hugging Face
- NLP
- Deep Learning
- Model inference
- FastAPI
Project 3 — Enterprise RAG
Repository
enterprise-rag
Demonstrates
- LLMs
- Embeddings
- RAG
- Vector databases
- PostgreSQL
- pgvector
- LangChain
- FastAPI
- Evaluation
Project 4 — AI Software Engineering Agent
Repository
ai-software-engineering-agent
Demonstrates
- Agents
- LangGraph
- Tool calling
- MCP
- GitHub
- Docker
- Code execution
- Multi-agent systems
Project 5 — Blockchain AI Research Agent
This is your differentiator.
Repository
blockchain-ai-research-agent
29. Blockchain AI Research Agent
Your existing blockchain experience makes this particularly valuable.
Architecture
User
│
↓
AI Orchestrator
│
┌─────────────┼─────────────┐
↓ ↓ ↓
Wallet Agent Contract Agent Market Agent
│ │ │
↓ ↓ ↓
EVM RPC ABI Analysis Market APIs
│ │ │
└─────────────┼─────────────┘
↓
LLM
↓
Research Report
Example capabilities
The user could ask:
Analyze this wallet's transaction history.
Explain this transaction.
What contracts interacted with this wallet?
Identify unusual activity.
Summarize the wallet's behavior over the last 30 days.
Explain this smart contract.
30. Recommended Stack
Programming
Python ⭐⭐⭐⭐⭐
TypeScript ⭐⭐⭐⭐⭐
Keep TypeScript.
Do not abandon your existing stack.
31. Machine Learning
Machine Learning ⭐⭐⭐⭐
Scikit-learn ⭐⭐⭐⭐
PyTorch ⭐⭐⭐
Statistics ⭐⭐⭐
32. Generative AI
LLMs ⭐⭐⭐⭐⭐
Prompt Engineering ⭐⭐⭐⭐⭐
Embeddings ⭐⭐⭐⭐⭐
RAG ⭐⭐⭐⭐⭐
Vector Databases ⭐⭐⭐⭐⭐
Fine-tuning ⭐⭐⭐
LoRA / QLoRA ⭐⭐⭐
LLM Evaluation ⭐⭐⭐⭐
33. Agentic AI
Tool Calling ⭐⭐⭐⭐⭐
LangGraph ⭐⭐⭐⭐⭐
Agents ⭐⭐⭐⭐⭐
MCP ⭐⭐⭐⭐
OpenAI Agents SDK ⭐⭐⭐⭐
Multi-Agent Systems ⭐⭐⭐
34. AI Infrastructure
FastAPI ⭐⭐⭐⭐⭐
Docker ⭐⭐⭐⭐⭐
AWS ⭐⭐⭐⭐
MLflow ⭐⭐⭐⭐
Model Deployment ⭐⭐⭐⭐
Observability ⭐⭐⭐⭐
CI/CD ⭐⭐⭐⭐⭐
35. Six-Month Weekly Schedule
Assuming approximately 10–15 hours/week:
| Day | Activity |
|---|---|
| Monday | Course — 1.5 hours |
| Tuesday | Course — 1.5 hours |
| Wednesday | Coding — 2 hours |
| Thursday | Course — 1.5 hours |
| Friday | Coding — 1.5 hours |
| Saturday | Project — 4 hours |
| Sunday | Project / revision — 3 hours |
Total:
~15 hours/week
Six months:
~360 hours
36. Monthly Milestones
End of Month 1
You should be able to say:
"I can build and deploy traditional ML models."
End of Month 2
You should be able to say:
"I understand neural networks and can build PyTorch models."
End of Month 3
You should be able to say:
"I can build LLM applications and RAG systems."
End of Month 4
You should be able to say:
"I can design, evaluate and deploy production RAG systems."
End of Month 5
You should be able to say:
"I can build agentic AI systems using LangGraph, tool calling and MCP."
End of Month 6
You should be able to say:
"I'm an experienced software engineer specializing in production AI/LLM systems."
37. Resume Skills
Do not put only:
AI
ChatGPT
Prompt Engineering
Instead, build a technical skills section like:
AI / ML
Python, PyTorch, Scikit-learn, Hugging Face,
Machine Learning, Deep Learning, NLP
Generative AI
LLMs, RAG, Embeddings, Vector Databases,
Fine-tuning, LoRA, QLoRA, LLM Evaluation
Agentic AI
LangGraph, LangChain, OpenAI Agents SDK,
MCP, Tool Calling, Multi-Agent Systems
AI Infrastructure
FastAPI, Docker, AWS, MLflow,
Model Deployment, Observability, CI/CD
38. Example Resume Positioning
Instead of:
Senior Software Engineer with experience in Node.js and blockchain.
Move toward:
Senior Software Engineer specializing in backend systems, cloud infrastructure and production-grade AI/LLM applications, with experience building RAG pipelines, agentic workflows, AI APIs and distributed systems.
Once you actually have the projects/experience to support it, you can use:
Senior Software Engineer / AI Engineer
or:
AI Engineer | LLMs | RAG | Agentic AI | AWS
39. LinkedIn Positioning
Headline
Senior Software Engineer | AI Engineer | LLMs | RAG | Agentic AI | AWS | Blockchain
Or:
Senior Software Engineer → AI Engineer | LLM Engineering | RAG | LangGraph | AWS
40. What NOT to Do
❌ Don't collect courses
Avoid:
20 Udemy courses
0 projects
Instead:
5 courses
4 strong projects
❌ Don't build only ChatGPT clones
Avoid:
Chatbot
Chatbot
Chatbot
Chatbot
Build systems demonstrating:
RAG
Agents
Tool Calling
Evaluation
Production Deployment
AI Infrastructure
❌ Don't learn every AI framework
You don't need expert knowledge of:
LangChain
LlamaIndex
CrewAI
AutoGen
Semantic Kernel
Haystack
DSPy
...
Choose a small number and go deep.
Recommended:
LangGraph
OpenAI Agents SDK
MCP
❌ Don't ignore evaluation
A production AI engineer must know:
"How do I know the model is producing good results?"
Evaluation is as important as prompting.
❌ Don't abandon your backend skills
Your existing backend experience is an advantage.
Keep:
Node.js
NestJS
TypeScript
Microservices
AWS
Docker
while adding:
Python
FastAPI
LLMs
RAG
Agents
MLOps
41. The Ideal Final Skill Profile
At the end of the roadmap:
AI ENGINEER
│
┌─────────────┼─────────────┐
│ │ │
Models Systems Production
│ │ │
ML RAG AWS
DL Agents Docker
PyTorch MCP CI/CD
Transformers Tools MLOps
NLP Evaluation Monitoring
│ │ │
└─────────────┼─────────────┘
│
AI Applications
42. Your Competitive Advantage
Do not try to compete with people who have PhDs in machine learning.
Your advantage is different.
You can combine:
Software Engineering
+
Backend Architecture
+
Cloud
+
Distributed Systems
+
Blockchain
+
LLMs
+
Agentic AI
This gives you a potentially very strong niche:
AI Engineer specializing in production AI systems and intelligent applications for distributed/financial systems.
43. Recommended Learning Order
If you want the shortest possible version of the roadmap:
1. Python
↓
2. Machine Learning
↓
3. PyTorch
↓
4. Transformers
↓
5. LLM Engineering
↓
6. Embeddings
↓
7. RAG
↓
8. Vector Databases
↓
9. LLM Evaluation
↓
10. LangChain
↓
11. LangGraph
↓
12. Tool Calling
↓
13. MCP
↓
14. Agentic AI
↓
15. MLOps
↓
16. Production AI
44. Course Priority
If time becomes limited:
Must do
- Machine Learning A-Z
- AI Engineer Core Track
- LangChain / LangGraph
Strongly recommended
- PyTorch for Deep Learning
- MLOps Zero to Hero
Optional
- AI Engineer Agentic Track
45. Final 6-Month Outcome
The objective is not:
"I completed some AI courses."
The objective is:
SENIOR SOFTWARE ENGINEER
│
│
6 MONTHS
↓
┌─────────────────────────┐
│ AI ENGINEER │
└─────────────────────────┘
│
┌───────────────────┼───────────────────┐
↓ ↓ ↓
ML / DL LLMs Production
│ │ │
PyTorch RAG AWS
Scikit-learn Agents Docker
Transformers MCP CI/CD
│ LangGraph MLOps
│ │ │
└───────────────────┼───────────────────┘
↓
3–5 Strong Projects
↓
AI Engineer Resume
↓
AI Job Interviews
The final portfolio should ideally contain:
- ML Churn Prediction
- Transformer/NLP Classifier
- Enterprise RAG Platform
- AI Software Engineering Agent
- Blockchain AI Research Agent
The last three are the most important for your career transition.
46. The Golden Rule
Learn enough theory to understand what you're building, then spend most of your time building it.
Your existing software-engineering experience means you should be able to move through the fundamentals faster than a complete beginner.
Your target isn't to become a researcher.
Your target is to become someone who can take:
Business Problem
↓
AI Architecture
↓
Model / LLM
↓
RAG / Agents
↓
Backend
↓
Cloud
↓
Deployment
↓
Monitoring
↓
Production
and build the entire system.
That is the profile I would target for your transition from Senior Software Engineer → AI Engineer.