Program Overview
Complete 12-Week End-to-End Plan designed to transform freshers into production-ready GenAI engineers. This intensive internship program combines hands-on projects, industry-standard practices, and career preparation to launch your career in AI engineering.
Duration
12 Weeks (25-30 hours/week)
Level
Freshers / Entry Level
Primary Language
Python 3.11+
Format
Intensive Internship
PHASE 1: FOUNDATIONS (Weeks 1-4)
Building Core GenAI Engineering Skills
Week 1: Python Excellence & AI-First Development
Objective: Master modern Python and AI-assisted development workflow
Python 3.11+ features (type hints, async/await, performance optimization)
AI-assisted coding tools (Cursor, GitHub Copilot, Claude)
Vibe coding paradigm (describe → AI generates → refine → deploy)
Deliverable: Production-ready text classification API with 90%+ test coverage, deployed live + Blog post on "Building with Vibe Coding"
Assessment: Code review + Performance benchmarks + DSA quiz
Week 2: LLMs, Embeddings & Vector Operations
Objective: Deep understanding of language models and vector mathematics
Transformer architecture (attention, tokenization, positional encoding)
LLM APIs (OpenAI, Anthropic, open-source models)
Embedding generation and similarity search
Vector mathematics (cosine similarity, distance metrics)
DSA: K-D Trees, LSH, HNSW, ANN algorithms
Deliverable: Production embedding service with caching + Multi-provider LLM wrapper with cost tracking
Assessment: Technical deep-dive presentation + DSA quiz
Week 3: Vector Databases & Advanced Retrieval
Objective: Master production vector search systems
Vector database internals (HNSW, IVF, PQ indexing)
Production vector stores (Qdrant, Weaviate, Pinecone, ChromaDB)
Document chunking strategies (fixed, semantic, agentic)
Hybrid search (vector + keyword + metadata filtering)
DSA: Graph algorithms, clustering, query optimization
Deliverable: Production vector search system with multi-strategy chunking pipeline, hybrid search with re-ranking, load test results (1000 QPS)
Assessment: System design interview + Implementation review
Week 4: RAG Architecture & Evaluation
Objective: Build enterprise-grade RAG systems
RAG system design patterns (basic → advanced)
Multi-stage retrieval (vector, keyword, graph)
Re-ranking and context optimization
RAG evaluation (RAGAS framework)
Advanced techniques (HyDE, multi-query, parent-child chunking)
DSA: Priority queues, result fusion, caching strategies
MAJOR PROJECT: Enterprise RAG System
Multi-format document support
Advanced retrieval strategies
RAGAS score > 0.85
Production-ready code
Complete documentation
Assessment: Code review + System design presentation + Live demo
PHASE 2: AGENTIC AI & MODELS (Weeks 5-8)
Building Intelligent Autonomous Systems
Week 5: Single-Agent Systems & Tool Development
Objective: Build production-ready autonomous agents
Agent architectures (ReAct, Plan-Execute, Self-Ask)
Tool development and validation (calculator, web search, database, files, APIs)
Agent memory systems (short-term, long-term, entity memory)
Error handling and loop detection
DSA: State machines, decision trees, graph search
Deliverable: Multi-tool agent system (5+ tools) with memory persistence (Redis + Vector DB + Neo4j), comprehensive error recovery, and interactive Streamlit demo
Assessment: Architecture quiz + Live coding + Memory design interview
Week 6: Multi-Agent Systems & MCP
Objective: Orchestrate multiple agents and build MCP servers
Multi-agent patterns (sequential, hierarchical, collaborative, debate)
Model Context Protocol (MCP) specification
MCP server implementation (database, filesystem, API integrations)
Agent communication and coordination
DSA: Message queues, task scheduling, distributed consensus
Deliverable: Multi-agent system (3+ specialized agents) with 2+ MCP servers (custom domain-specific), inter-agent communication protocols, production architecture
Assessment: System design exam + Code review + Live demo
Week 7: Small Language Model Training
Objective: Train a language model from scratch
Transformer architecture implementation (attention, feed-forward, layer norm)
Training pipeline (data loading, optimization, checkpointing)
Tokenization
Model training (50M-100M parameters)
Evaluation and perplexity metrics
DSA: Matrix operations, gradient algorithms, memory optimization
MAJOR PROJECT: Trained Small Language Model
50M-100M parameters
Complete training code
Evaluation metrics
HuggingFace model card
Training report with analysis
Assessment: Model performance + Code quality + Technical report
Week 8: Fine-Tuning & Model Optimization
Objective: Fine-tune models and optimize for production
Fine-tuning techniques (LoRA, QLoRA, adapters)
Dataset curation for instruction tuning
Advanced techniques (RLHF, DPO)
Model serving (vLLM, Text Generation Inference)
Quantization (8-bit, 4-bit, GPTQ, AWQ)
DSA: Matrix decomposition, quantization algorithms
Deliverable: Domain-specific fine-tuned model, optimized for inference, deployed with vLLM/TGI, performance benchmarks, cost analysis
Assessment: Fine-tuning exam + Model deployment review
PHASE 3: PRODUCTION & CAREER (Weeks 9-12)
Deploying at Scale & Launching Career
Week 9: LLMOps
Objective: Build production-grade AI applications
Software engineering best practices (SOLID, design patterns, clean code)
CI/CD for AI applications (GitHub Actions, Docker, Kubernetes)
Monitoring and observability
LLM-specific metrics and cost tracking
Testing strategies (unit, integration, LLM evaluation)
DSA: Design patterns implementation
Deliverable: Production application with complete CI/CD pipeline, 90%+ test coverage, comprehensive monitoring, security hardening, deployed to cloud
Assessment: Code review + DevOps quiz + Live demo
Week 10: System Design & Scalability
Objective: Design scalable distributed AI systems
Distributed systems for AI (microservices, load balancing, caching)
Performance optimization (profiling, async patterns, connection pooling)
Security and compliance (OAuth2, rate limiting, GDPR)
Database optimization and sharding
Disaster recovery planning
DSA: Consistent hashing, cache eviction, distributed algorithms
Deliverable: Scalable system design document with architecture diagrams (C4 model), performance benchmarks, security audit report, disaster recovery plan
Assessment: System design interview (2 problems, 90 minutes)
Week 11: Specialized Tracks
Objective: Deep dive into advanced specialization - Choose ONE Track
Track A: Advanced RAG & Knowledge Graphs
Graph databases (Neo4j)
Knowledge graph construction
Graph RAG architecture
Multi-hop reasoning
Hybrid retrieval (vector + graph + keyword)
Track B: Code Intelligence & AST
Abstract Syntax Trees
Tree-sitter for multi-language parsing
Language Server Protocol
Semantic code search
Code generation agents
Track C: Multimodal AI
Vision-language models
Document understanding with vision
Multimodal RAG
Audio processing (Whisper)
Visual question answering
Deliverable: Advanced project in chosen track with research-level implementation, production deployment, performance analysis
Assessment: Technical deep-dive + Specialization exam
Week 12: Capstone Project & Career Launch
Objective: Build portfolio project and launch career
Days 1-10: Capstone Project Development
Project Categories (Choose ONE):
Enterprise AI Platform: Multi-tenant RAG system or agent orchestration platformDomain-Specific AI: Healthcare/Finance/Legal/Education AI assistantDeveloper Tools: AI-powered code reviewer or documentation generatorAdvanced Research: Novel agent system or custom model architecture
Technical Requirements:
✅ Fine-tuned OR trained small model included
✅ Complete CI/CD pipeline
✅ Monitoring and observability
✅ Security best practices
✅ 90%+ test coverage
✅ Production deployment
✅ Comprehensive documentation
Days 11-12: Documentation
System architecture document (C4 diagrams)
Technical specification (API docs, database schema)
Implementation report (design decisions, challenges, benchmarks)
Code documentation (README, deployment guide)
Days 13-14: Career Preparation
Technical Interview Prep:
10+ coding problems (arrays, graphs, DP, strings)
5+ system design scenarios
Mock interviews
Code review simulations
Behavioral Interview Prep:
STAR method for projects
Leadership examples
Handling failure stories
Technical communication
Portfolio Finalization:
GitHub profile optimization
LinkedIn with GenAI endorsements
Portfolio website
Resume tailoring
Days 15-16: Final Presentations & Graduation
Presentation Format (20 minutes):
Problem statement (2 min) → Architecture overview (5 min) → Live demo (7 min) → Technical deep-dive (4 min) → Q&A (2 min)
Evaluation Criteria:
Technical implementation (30%) | Code quality & architecture (25%) | Innovation & complexity (20%) | Documentation (15%) | Presentation (10%)
Graduation Requirements
✅
10+ weekly projects submitted
✅
Capstone project score > 75%
✅
DSA assessment > 70%
✅
System design assessment > 70%
✅
90%+ attendance
✅
Active code review participation
Upon Graduation, You Receive:
Certificate of Completion (with grade)
Skill Badges (RAG, Agents, LLM Training, MCP, System Design)
LinkedIn endorsements from mentors
Portfolio review with feedback
Letter of recommendation (top 10%)
Access to alumni network and partner companies
6 months continued community access
Assessment & Grading
50%
Weekly Assessments
Code quality reviews
DSA quizzes
Project submissions
Peer reviews
Technical presentations
30%
Phase Checkpoints
Phase 1 (Week 4): Foundations exam
Phase 2 (Week 8): Advanced skills evaluation
Phase 3 (Week 12): Final assessment
20%
Capstone Project
Technical implementation
Code quality
Documentation
Presentation
Innovation
Career Outcomes
Target Roles
GenAI Engineer / Developer
LLM Application Engineer
AI/ML Engineer (LLM focus)
RAG Systems Developer
AI Agent Developer
MLOps Engineer (LLM)
AI Platform Engineer
Expected Compensation
India (Internship)
₹8-15 LPA
India (Full-time)
₹15-25 LPA
US (Entry-level)
$80K-$120K
Remote (Global)
$60K-$100K
Target Companies
AI Startups (rapid growth, learning)
Product Companies
Consulting Firms (diverse projects)
Enterprise (stability, resources)
Technology Stack
Core Development
Python 3.11+, PyTorch, Hugging Face, FastAPI, Pydantic
LLM & AI
OpenAI/Anthropic APIs, LangChain/LlamaIndex, Sentence Transformers, vLLM/TGI, PEFT
Vector & Databases
ChromaDB/Qdrant, FAISS, PostgreSQL, Redis, Neo4j
DevOps & Tools
Docker/Kubernetes, GitHub Actions, Prometheus/Grafana, pytest, black/flake8/mypy
Cloud Platforms
AWS/Azure/GCP, Render/Railway (free tier)
Weekly Time Commitment
Total: 25-30 hours/week
6-8h
Lectures/Tutorials
8-10h
Hands-on Labs
6-8h
Project Work
2-3h
Assessments/Reviews
3-4h
Self-Study/Practice
Program Philosophy
1
Fundamentals First
Strong DSA + System Design foundation
2
AI-Augmented Learning
Use AI tools throughout (Claude, Cursor, Copilot)
3
Production Mindset
Everything deployable, everything scalable
4
Agent-First Thinking
Build systems that build systems
5
Portfolio-Driven
15+ projects demonstrating real capability
Success Metrics
90%+
Completion rate
150%+
Average salary increase
15+
Production projects
80%+
Interview success rate
This Program Is Designed For
Corporate training partnerships
University collaborations
Serious career transformation in Gen-AI/LLM engineering
Students ready to commit 25-30 hours/week for 12 weeks
Building production-ready engineers, not tutorial followers