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IntermediateAI & Machine Learning

AI Engineer Roadmap

Build intelligent systems with LLMs and AI agents

Duration
8-10 months
Job Demand
very high
Phases
6
Views
1844

About This Roadmap

AI Engineering is an emerging role focused on building practical AI applications using large language models (LLMs), AI agents, and modern AI tools. As an AI engineer, you will integrate LLMs into applications, design prompts, build AI agents, implement RAG (Retrieval Augmented Generation), fine-tune models, and create AI-powered features. This comprehensive roadmap covers LLM fundamentals, prompt engineering, LangChain, vector databases, AI agents, fine-tuning, AI APIs (OpenAI, Anthropic, Google), and AI application architecture. AI engineers are in extremely high demand as companies race to integrate AI into their products. The role requires strong programming skills, understanding of AI/ML concepts, creativity in prompt design, and ability to build production AI systems. Unlike ML engineers who focus on training models, AI engineers focus on using pre-trained models to build applications.

Prerequisites

  • Programming skills (Python)
  • Basic ML understanding
  • API integration experience
  • Problem-solving mindset

What You'll Learn

Master LLMs
Design effective prompts
Build AI agents
Implement RAG
Fine-tune models
Use AI APIs
Deploy AI apps

Complete Learning Path

6-8 weeks

1
ML Basics

Supervised, unsupervised, deep learning overview

2
Neural Networks

Architecture, training, transformers

3
LLM Basics

GPT, BERT, how LLMs work, tokenization

4
Python for AI

Libraries, APIs, async programming

6-8 weeks

1
Prompt Basics

Zero-shot, few-shot, chain-of-thought

2
Advanced Prompting

ReAct, tree of thoughts, self-consistency

3
Prompt Optimization

Testing, iteration, evaluation

4
AI APIs

OpenAI, Anthropic, Google Gemini, API usage

8-10 weeks

1
LangChain Basics

Chains, prompts, output parsers

2
Memory

Conversation memory, buffer, summary

3
Tools & Agents

Function calling, tool use, agents

4
LangSmith

Debugging, monitoring, evaluation

8-10 weeks

1
Vector Databases

Pinecone, Weaviate, Chroma, embeddings

2
RAG Architecture

Retrieval, generation, chunking strategies

3
Document Processing

PDF, web scraping, text extraction

4
Semantic Search

Embeddings, similarity search, reranking

8-10 weeks

1
Agent Architecture

ReAct, planning, tool use

2
Multi-Agent Systems

Agent collaboration, orchestration

3
AutoGPT/BabyAGI

Autonomous agents, task decomposition

4
Agent Frameworks

CrewAI, AutoGen, custom agents

6-8 weeks

1
Fine-tuning

LoRA, QLoRA, instruction tuning

2
Model Optimization

Quantization, distillation, inference

3
Deployment

FastAPI, Docker, cloud deployment

4
Monitoring

Cost, latency, quality, observability

Tools & Technologies

PythonLangChainOpenAI APIAnthropicPineconeWeaviateHugging FaceFastAPIDockerVector DBs

Career Opportunities

  • AI Engineer
  • LLM Engineer
  • AI Application Developer
  • Prompt Engineer
  • AI Product Engineer
  • AI Architect

Expected Salary (India)

₹10-18 LPA (Entry)
₹18-35 LPA (Mid)
₹35-70 LPA (Senior)
₹70L+ (Lead)

Companies Hiring

OpenAIAnthropicGoogleMicrosoftMetaAmazonStartupsAll companies adopting AI

Ready to Start?

Follow this roadmap step-by-step and track your progress.

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