Generative AI Engineer Master Program
An intensive 4-month online weekend live engineering cohort (Sat & Sun • 4 hours/day: 2h live faculty lectures + 2h supervised coding labs) covering Generative AI Engineer production capstones and 580+ AI interviews, with eligible hiring-drive access under documented placement terms and tuition-refund protection where all policy conditions are met.
- Online Weekend Live Sessions (Sat & Sun • 4 Hours / Day)
- 4 Months (16 Weeks) Comprehensive Finish
- Guaranteed Placement Drives until Placed across 1,050+ top companies
- 26 Production Microservices & Capstones with GitHub code reviews
- 580+ Topic Mock Tests & 1-on-1 AI Voice Technical Interviews
Our Graduates Get Marketed to 1,050+ Global Tech Leaders & Unicorns
Continuous corporate interview referrals until job offer letter issuance:
Online Weekend Master Track: 4 Months of Live Mentorship & Placement Drives
Designed for college students and working professionals, our Online Weekend Master Track delivers intensive 4-hour live sessions every Saturday and Sunday across 16 weeks (4 Months). Complete 128+ hours of live faculty instruction, 26 production capstones, and 580+ AI interviews with unlimited corporate interview drives until you get placed!
Live Architecture & Core Mentorship
2 Hours of interactive enterprise architecture, live faculty coding, and design patterns followed by 2 Hours of supervised capstone development.
Hands-On Labs, Tests & AI Practice
2 Hours of advanced microservices, real-time queues & cloud deployment followed by 2 Hours of timed mock tests and 1-on-1 AI voice interview rounds.
4-Month Master Roadmap to Guaranteed Placement
We transform you into a battle-tested software engineer ready to clear Tier-1 technical and system design interview rounds in 4 months with weekend online sessions.
Architecture & Core Mechanics
Core OOP, memory mechanics, data structures, algorithms & clean design patterns.
Capstones & Microservices
Build production full stack SaaS, microservices, REST APIs, queues & cloud deployment.
System Design & AI Interviews
Simulate live FAANG interview rounds, timed topic mock tests and AI voice evaluations.
Corporate Drives until Placed
Resume marketing, hiring drives across 1,050+ partners, and placement guarantee.
Projected Target CTC After Program
Industry-verified compensation brackets achieved by graduates across 1,050+ hiring partners:
₹8.5L – ₹14L /yr
Software Engineer I, Junior Backend Developer, Full Stack Associate.
- 260+ Hours Live Mentorship
- 26 Capstone Projects on GitHub
- 580+ AI Technical Interview Scorecards
₹14L – ₹24L /yr
Full Stack Java Engineer, Spring Boot Microservices Specialist, Cloud Engineer.
- Kafka Event-Driven Architectures
- Redis Caching & Performance Tuning
- Docker, Kubernetes & AWS CI/CD
₹24L – ₹36L+ /yr
Senior Full Stack Engineer, Microservices Architect, Lead Consultant.
- High-Throughput System Design (LLD/HLD)
- Fault Tolerance & Distributed Transactions
- FAANG System Design Clearing Mentorship
Topic-Wise Curriculum & Practice Hub
20 Modules • 40 Deep-Dive Topics • 40 Integrated Topic Mock Tests & AI Interviews
Career orientation, development environment, production-ready Python and Git/GitHub workflows.
Generative AI Career Orientation
Artificial Intelligence; AI vs ML vs Deep Learning vs Generative AI; traditional vs generative systems; AI evolution and ecosystem; enterprise GenAI applications; AI Engineer and GenAI Engineer responsibilities; LLM Engineer vs ML Engineer; career roadmap; AI application architecture; AI APIs; development environment setup.
- Explain the core concepts and architecture of Generative AI Career Orientation.
- Apply Generative AI Career Orientation techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Python Programming for AI
Variables, data types, operators, conditions, loops, functions, collections, strings, files, exceptions, modules, packages, virtual environments, OOP, classes, inheritance, encapsulation, polymorphism, decorators, iterators, generators, lambdas, comprehensions, type hints, dataclasses, JSON, environment variables, API calls, async/await, HTTP clients, dependency management, Pydantic, structured models, logging and configuration.
- Explain the core concepts and architecture of Python Programming for AI.
- Apply Python Programming for AI techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Git & GitHub for AI Engineers
Git architecture; repositories; clone, commit, push and pull; branches; merges; pull requests; conflict resolution; .gitignore; README files; documentation; AI project versioning; environment-secret protection.
- Explain the core concepts and architecture of Git & GitHub for AI Engineers.
- Apply Git & GitHub for AI Engineers techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Essential machine-learning, neural-network, deep-learning and NLP concepts needed for LLM engineering.
Machine Learning Essentials for GenAI Engineers
Machine-learning fundamentals; supervised and unsupervised learning; classification; regression; training vs inference; features; labels; datasets; validation and testing; overfitting and underfitting; loss functions; model parameters and hyperparameters; model evaluation.
- Explain the core concepts and architecture of Machine Learning Essentials for GenAI Engineers.
- Apply Machine Learning Essentials for GenAI Engineers techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Neural Networks & Deep Learning Fundamentals
Biological inspiration; artificial neurons; layers; weights; biases; activation functions; forward propagation; backpropagation; gradient descent; embeddings; neural-network training; deep-learning architectures; GPU basics.
- Explain the core concepts and architecture of Neural Networks & Deep Learning Fundamentals.
- Apply Neural Networks & Deep Learning Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
NLP Fundamentals
Natural Language Processing; text preprocessing; tokenization; stop words; stemming; lemmatization; vocabulary; n-grams; word embeddings; semantic similarity; sentence embeddings; transformer introduction.
- Explain the core concepts and architecture of NLP Fundamentals.
- Apply NLP Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Transformer architecture, foundation models, inference controls and the modern LLM ecosystem.
Transformer Architecture
Sequence models; attention and self-attention; query, key and value; multi-head attention; positional encoding; encoders; decoders; transformer blocks; context windows; tokens; tokenizers; next-token prediction.
- Explain the core concepts and architecture of Transformer Architecture.
- Apply Transformer Architecture techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Large Language Models
LLMs and foundation models; pretraining; instruction tuning; fine-tuning; reinforcement-learning concepts; inference; temperature; top-p; top-k; maximum output tokens; context windows; structured output; hallucinations; deterministic vs probabilistic output; commercial, open-weight, small, large, reasoning and multimodal models.
- Explain the core concepts and architecture of Large Language Models.
- Apply Large Language Models techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Professional prompt design, context management, structured outputs, function calling and tool execution.
Professional Prompt Engineering
Prompt anatomy; instruction design; context engineering; role, zero-shot, one-shot and few-shot prompting; delimiters; constraints; output formats; examples; structured prompts; JSON; templates; chaining; context management; prompt-injection awareness; prompt testing.
- Explain the core concepts and architecture of Professional Prompt Engineering.
- Apply Professional Prompt Engineering techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Structured Outputs & Function Calling
Structured JSON generation; schema validation; Pydantic models; reliable outputs; function calling; tool calling; function schemas; tool execution; error handling; tool chaining.
- Explain the core concepts and architecture of Structured Outputs & Function Calling.
- Apply Structured Outputs & Function Calling techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Provider-neutral LLM APIs and production-style FastAPI backends with validation, streaming and resilience.
LLM API Development
API authentication and keys; environment variables; request-response lifecycle; chat APIs; message roles; streaming; error handling; retry logic; rate limiting; token management; cost calculation; API abstraction; provider independence.
- Explain the core concepts and architecture of LLM API Development.
- Apply LLM API Development techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
FastAPI for AI Applications
FastAPI architecture; routes; path and query parameters; request bodies; Pydantic; validation; dependency injection; authentication concepts; middleware; exception handlers; async APIs; streaming responses; REST architecture; API documentation; CORS; health checks.
- Explain the core concepts and architecture of FastAPI for AI Applications.
- Apply FastAPI for AI Applications techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Semantic representations, similarity search, vector indexing, metadata filters and vector-store selection.
Embeddings
Embeddings; vector representations; semantic meaning; embedding models; dimensions; similarity search; cosine similarity; dot product; Euclidean distance; document and query embeddings.
- Explain the core concepts and architecture of Embeddings.
- Apply Embeddings techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Vector Databases
Vector databases; indexing; similarity search; metadata and filtering; collections; namespaces; persistence; FAISS; Chroma; pgvector; Pinecone and managed-vector-platform concepts; vector database selection.
- Explain the core concepts and architecture of Vector Databases.
- Apply Vector Databases techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
End-to-end RAG from ingestion and chunking through advanced retrieval, citations and enterprise assistants.
RAG Fundamentals
RAG purpose and architecture; knowledge sources; document ingestion; parsing; chunking; embeddings; indexing; retrieval; prompt augmentation; generation; citations; grounded answers; document -> parsing -> chunking -> embedding -> vector database -> retrieval -> LLM -> answer pipeline.
- Explain the core concepts and architecture of RAG Fundamentals.
- Apply RAG Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Document Ingestion
PDF, DOCX, TXT, CSV, HTML, Markdown and JSON ingestion; text and metadata extraction; document loaders; cleaning; preprocessing; duplicate detection; document IDs; chunk provenance.
- Explain the core concepts and architecture of Document Ingestion.
- Apply Document Ingestion techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Advanced Chunking
Fixed-size, recursive, semantic, sentence, parent-child and Markdown-aware chunking.
- Explain the core concepts and architecture of Advanced Chunking.
- Apply Advanced Chunking techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Advanced Retrieval
Top-K retrieval; similarity thresholds; metadata filtering; hybrid keyword and semantic search; reranking; query rewriting; multi-query retrieval; context compression; parent-document retrieval; citation generation.
- Explain the core concepts and architecture of Advanced Retrieval.
- Apply Advanced Retrieval techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Advanced RAG
Conversational, hybrid and multi-source RAG; query decomposition; reranking; RAG fusion; corrective and adaptive RAG concepts; graph retrieval; SQL + RAG; APIs + RAG; multimodal and enterprise RAG.
- Explain the core concepts and architecture of Advanced RAG.
- Apply Advanced RAG techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Composable model, prompt, parser, chain, retriever, vector-store, tool and callback abstractions.
LangChain Fundamentals
Components; models; prompts; parsers; chains; retrievers; document loaders; embeddings; vector stores; tools; callbacks; structured outputs.
- Explain the core concepts and architecture of LangChain Fundamentals.
- Apply LangChain Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Agent reasoning loops, planning, tools, observations, memory, safeguards and human approval.
AI Agent Fundamentals
Agents vs chatbots; agent loop; reasoning; planning; actions; tools; observations; memory; state; goals; environment; autonomous execution; human approval.
- Explain the core concepts and architecture of AI Agent Fundamentals.
- Apply AI Agent Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Tool-Using AI Agents
Search, calculator, database, REST API, file-system concepts, business APIs and custom functions; tool selection and routing; failures and retries; permissions and safeguards.
- Explain the core concepts and architecture of Tool-Using AI Agents.
- Apply Tool-Using AI Agents techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Stateful graph workflows, checkpoints, human-in-the-loop controls and multi-agent orchestration.
LangGraph
Graph concepts; nodes; edges; state; conditional routing; workflow design; persistence; checkpoints; human-in-the-loop; retries; state machines; deterministic and agentic workflows.
- Explain the core concepts and architecture of LangGraph.
- Apply LangGraph techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Multi-Agent Systems
Single vs multi-agent systems; supervisor pattern; specialist agents; planner; executor; reviewer; routing agents; collaborative workflows; shared context; orchestration; agent communication; failure handling.
- Explain the core concepts and architecture of Multi-Agent Systems.
- Apply Multi-Agent Systems techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
MCP architecture, clients, servers, tools, resources, prompts, transports and secure interoperability.
MCP Fundamentals
Why Model Context Protocol exists; MCP architecture; clients; servers; tools; resources; prompts; transport concepts; exposing external capabilities; secure integrations; agent interoperability.
- Explain the core concepts and architecture of MCP Fundamentals.
- Apply MCP Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Short- and long-term memory plus deliberate management of retrieved, conversational and tool context.
AI Memory
Conversation, short-term, long-term and semantic memory; episodic concepts; persistent state; user preferences; memory retrieval; summarization; privacy.
- Explain the core concepts and architecture of AI Memory.
- Apply AI Memory techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Context Engineering
Prompt, retrieved and conversation context; system instructions; tool outputs; memory; context windows; prioritization; compression; context pollution; context-management strategy.
- Explain the core concepts and architecture of Context Engineering.
- Apply Context Engineering techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Text, image, audio, document, vision and speech workflows for multimodal applications and RAG.
Multimodal AI
Text, images, audio, documents, vision and speech; document intelligence; image analysis; screenshot understanding; visual question answering; voice-enabled assistants; multimodal RAG.
- Explain the core concepts and architecture of Multimodal AI.
- Apply Multimodal AI techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Golden datasets, human and automated evaluation, LLM judges, RAG metrics and regression testing.
LLM Evaluation
Why evaluation matters; ground truth; golden datasets; human and automated evaluation; LLM-as-a-judge concepts; relevance; correctness; faithfulness; groundedness; answer quality; hallucination detection; retrieval precision and recall; prompt evaluation; regression tests; A/B experiments.
- Explain the core concepts and architecture of LLM Evaluation.
- Apply LLM Evaluation techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
RAG Evaluation
Retrieval relevance; context relevance; answer relevance; faithfulness; citation correctness.
- Explain the core concepts and architecture of RAG Evaluation.
- Apply RAG Evaluation techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Prompt attacks, malicious content, tool permissions, privacy, responsible AI and safe deployment.
Generative AI Security
Prompt injection; indirect prompt injection; jailbreak awareness; sensitive-data exposure; system-prompt leakage; malicious documents; insecure tool execution; excessive permissions; data poisoning concepts; output validation; access control; secrets management; PII protection; rate limiting.
- Explain the core concepts and architecture of Generative AI Security.
- Apply Generative AI Security techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Responsible AI
Bias; fairness; privacy; transparency; explainability concepts; hallucinations; misinformation; copyright considerations; human oversight; responsible deployment.
- Explain the core concepts and architecture of Responsible AI.
- Apply Responsible AI techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Layered production architecture from frontend and API gateway through AI orchestration, data and observability.
Production GenAI Architecture
User -> frontend -> API gateway -> application backend -> AI orchestration -> LLM/agents/RAG -> vector database/SQL/APIs -> observability; layered and clean architecture; service boundaries; AI gateway; provider abstraction; fallback models; caching; queues; scalability; reliability; latency optimization; error handling.
- Explain the core concepts and architecture of Production GenAI Architecture.
- Apply Production GenAI Architecture techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Token budgets, routing, caching, compression, latency, streaming, concurrency and resilient fallbacks.
Token and Cost Optimization
Input and output tokens; token budgeting; model selection; caching; summarization; prompt compression; conversation truncation; batching concepts; routing; cost dashboards; usage quotas.
- Explain the core concepts and architecture of Token and Cost Optimization.
- Apply Token and Cost Optimization techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Performance Optimization
Latency; streaming; caching; asynchronous calls; parallel execution; vector-search optimization; retry strategies; fallback strategies.
- Explain the core concepts and architecture of Performance Optimization.
- Apply Performance Optimization techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Lifecycle management, versioning, deployment, monitoring, experiments, rollback and AI observability.
LLMOps Fundamentals
Development lifecycle; prompt and model versioning; evaluation; deployment; monitoring; feedback; experimentation; datasets; observability; rollback; regression testing.
- Explain the core concepts and architecture of LLMOps Fundamentals.
- Apply LLMOps Fundamentals techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
AI Observability
Request count; latency; tokens; model cost; errors; tool failures; retrieval quality; user feedback; hallucination signals; tracing; logs; metrics; dashboards.
- Explain the core concepts and architecture of AI Observability.
- Apply AI Observability techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
Containerization and AWS deployment for production-style GenAI applications.
Docker for GenAI
Containers; Dockerfiles; images; containers; volumes; networking; environment variables; Docker Compose; containerizing FastAPI; deployment configuration.
- Explain the core concepts and architecture of Docker for GenAI.
- Apply Docker for GenAI techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
AWS for GenAI Applications
Cloud fundamentals; IAM; EC2; S3; RDS; networking concepts; secrets; logging; monitoring; containers; serverless concepts; managed AI service concepts.
- Explain the core concepts and architecture of AWS for GenAI Applications.
- Apply AWS for GenAI Applications techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
An enterprise AI knowledge and automation platform combining the complete program.
Enterprise AI Knowledge & Automation Platform
Authentication with signup, login and roles; knowledge management for PDF, DOCX and URLs with metadata and indexing; multi-model abstraction; RAG, conversations, citations and structured outputs; research, document, database and workflow agents; PostgreSQL, vector database and Redis concepts; access control, protected documents and secrets; RAG and prompt evaluation with feedback; token, latency and error observability; Docker, AWS and GitHub deployment.
- Explain the core concepts and architecture of Enterprise AI Knowledge & Automation Platform.
- Apply Enterprise AI Knowledge & Automation Platform techniques in practical Generative AI applications.
- Evaluate implementation trade-offs, failure modes and production considerations.
26 Hands-On Production Capstones & Microservices
Build, deploy, and showcase real-world enterprise architectures on GitHub to prove your production engineering readiness:
Enterprise E-Commerce Microservices & Event-Driven Streaming Platform
Architected with Spring Boot 3.3, Apache Kafka event streams, Redis distributed caching, React 19 UI, and PostgreSQL. Features distributed ACID transaction sagas, payment webhook handling, dynamic inventory locking, and Dockerized Kubernetes deployment.
High-Throughput Banking & Core Transaction Engine
Concurrent multithreaded financial transaction ledger with ACID compliance, optimistic row locking, idempotent payment endpoints, and audit logging.
Real-Time Logistics & Fleet Tracking Service
Bi-directional live vehicle telemetry dashboard processing 10,000+ geo-coordinate events/sec with live map rendering and ETA calculations.
Multi-Tenant SaaS Subscription & Webhook Gateway
Multi-tenant automated billing engine with webhook signature verification, dynamic token bucket rate-limiting, and tenant data isolation schemas.
Distributed URL Shortener & Analytics System (Bitly Scale)
Low-latency URL redirection engine with distributed ID generation (Snowflake), sub-5ms Redis caching, and real-time click analytics.
Automated Cloud DevOps CI/CD Pipeline on AWS
Production containerization pipeline with automated testing, sonar code quality gates, container image vulnerability scanning, and zero-downtime rolling deploys.
AI-Powered Code Reviewer & Assessment Engine
Automated coding interview evaluator that parses Java AST trees, detects algorithmic time complexity, and simulates 1-on-1 voice technical interview feedback.
MockAttempt Academy vs. Traditional Bootcamps & Self-Study
Transparent side-by-side comparison of daily schedule, duration, curriculum, and placement support:
| Feature & Deliverables | MockAttempt Fast-Track Track | Expensive Bootcamps | Self-Study / YouTube |
|---|---|---|---|
| Live Weekend Schedule | Sat & Sun (4 Hours / Day) | 1 - 1.5 Hours / Day | Self-Paced / Inconsistent |
| Duration to Placement Readiness | 4 Months (16 Weeks Weekend) | 6 - 9 Months | 12+ Months (Uncertain) |
| Candidate Placement Guarantee | Unlimited Drives until Placed (or Full Refund) | Limited to 3-6 Months only | None (Apply blindly) |
| Topic Mock Tests & AI Interviews | Integrated for Every Topic (580+ Rounds) | End of Course Only | None |
| Tuition Fee | ₹49,999 ₹98,999 | ₹1,20,000 - ₹2,50,000 | Free (No Mentorship/Jobs) |
Institutional Course Assurances & 100% Placement Policy
100% Job Placement Guarantee
Our placement team arranges unlimited corporate interview drives across 1,050+ hiring partners until you receive an official offer letter. If unplaced, 100% of your tuition fee is refunded.
1-on-1 Expert Faculty Mentorship
Every cohort is taught live by seasoned lead architects from Tier-1 product companies with daily live coding and supervised code reviews.
Frequently Asked Questions
Ready to Become a Top 1% Generative AI Engineer in 3 Months?
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