Generative AI, Large Language Models (LLM) & Agentic AI Full Stack Engineering
GenAI Full Stack Developer + AWS + DevOps | 100% Placement Guaranteed Career Program | 100% Placement Guaranteed* (6 Months Fees Refund*)
Technologies You Will Master
100% Fees Refund* If Not Placed Within 6 Months
We believe in real career outcomes, not just empty certificates. If you complete the course modules, project milestones, and mock interview assessments but do not secure an IT engineering placement within 6 Months of course completion, MockAttempt guarantees a 100% full refund of your tuition fees* directly to your bank account with zero deductions.
Program Overview & Architectural Focus
### 🛡️ 100% Placement Assurance & Fees Refund Guarantee* (6-Month Window)
MockAttempt officially guarantees that upon graduating from this program and clearing the Job-Ready Assessment Pipeline, you will secure an IT engineering placement within 6 Months of course completion. If you are not placed within this 6-month post-completion period, you receive a **100% full refund of your course tuition fees*** directly into your bank account.
### 🎯 Multi-Dimensional Mastery & Assessment Framework (All Topics)
To ensure every graduate is genuinely world-class and job-ready for tier-1 enterprises:
- **Daily Topic Mock Tests (>98% Score Required)**: For every daily topic, students must score over 98% in proctored mock assessments. Students have access to **100+ dedicated Mock Tests per topic** to practice thoroughly.
- **Daily Topic-Wise AI Technical Interviews (>98% Score Required)**: Candidates undergo targeted AI technical voice interviews strictly testing that day's concept, requiring a >98% score.
- **Final Exit Benchmark (≥95% Minimum Score)**: To prove successful program completion and activate placement drives, candidates must achieve at least 95% in the comprehensive Final AI Interview and Mock Exam.
- **Multiple Re-Attempt Opportunities**: Students receive multiple chances to re-take assessments. Our goal is not just testing rote theoretical concepts, but rigorously developing **logical skills, problem-solving skills, and communication/articulation skills**.
*\*Terms & Conditions Apply. Subject to program milestones, attendance, project defense, topic mastery criteria, and active participation in placement drives.*
### 🎯 Target Engineering Roles & Audience
Software Developers, Backend Engineers, Data Scientists, and Ambitious Graduates aiming for high-impact Generative AI Engineer, LLM Application Developer, and AI Full Stack roles.
Enterprise Technologies & Toolchain Mastered
Production-level toolchain and engineering ecosystems practiced during hands-on lab sessions, code reviews, and capstone engineering.
Structured Learning Path Roadmap (10 Phases)
Structured step-by-step roadmap designed by senior engineers to take you from core concepts to interview-ready software engineering.
Phase 1 — Programming Foundations & Python Mastery
Build rock-solid computing and software fundamentals, mastery over Python 3.12 syntax, object-oriented principles, magic methods, modern functional programming, asyncio coroutines, Git version control, and data structures & algorithms.
Phase 2 — Databases, SQL & PostgreSQL for AI
Master relational database schema design, complex joins, window functions, query execution optimization, and advanced PostgreSQL capabilities for storing conversation state, prompts, AI responses, and metadata.
Phase 3 — Modern Frontend & AI User Interfaces
Architect modern, responsive single-page applications with React 19, TypeScript, and Redux Toolkit. Build ChatGPT-style streaming chat interfaces, prompt playgrounds, model comparison views, and AI analytics dashboards.
Phase 4 — Enterprise Backend & REST APIs
Develop high-throughput asynchronous APIs with FastAPI and Pydantic v2, robust admin portals with Django & Django REST Framework, streaming endpoints, WebSockets, and secure them with JWT tokens, refresh rotation, and RBAC.
Phase 5 — Mathematics, ML & Deep Learning Foundations
Master the mathematical foundations of AI (vectors, dot products, gradients, probability), data analysis with NumPy/Pandas, classical ML with Scikit-Learn, neural networks with PyTorch, NLP fundamentals, and Transformer self-attention architecture.
Phase 6 — Large Language Models, Prompting & Vector Databases
Deep dive into LLM capabilities, pretraining, context windows, API integrations, streaming responses, zero/few-shot prompting, chain-of-thought, semantic embeddings, and vector databases (pgvector, Chroma, Qdrant, FAISS).
Phase 7 — Production RAG, Document AI & Multimodal Intelligence
Construct enterprise Retrieval-Augmented Generation (RAG) pipelines with hybrid search, HyDE, rerankers, and citation groundings. Process complex PDFs with OCR/tables, develop multimodal vision/speech assistants, and index codebases for code RAG.
Phase 8 — Autonomous AI Agents, Tool Calling, MCP & AI Memory
Design autonomous AI agents capable of multi-step planning, tool selection, and self-correction. Master JSON schema tool calling, agent workflows (ReAct, Router, Supervisor), Model Context Protocol (MCP) clients/servers, and persistent multi-tier memory.
Phase 9 — Distributed Systems, Evaluation, Observability & LLMOps
Implement rigorous RAG and agent evaluation with Ragas/TruLens, end-to-end tracing and latency monitoring, LLM security guardrails (prompt injection defense), Redis semantic caching, Celery workers, Kafka event streaming, MLOps/LLMOps pipelines, and cost optimization.
Phase 10 — DevOps, AWS Cloud Infrastructure & Career Launch
Containerize and orchestrate AI applications using Docker and Kubernetes (EKS). Provision infrastructure with Terraform on AWS (EC2, VPC, S3, RDS, DynamoDB, Lambda, Bedrock). Build 8 portfolio projects and the enterprise Capstone platform, ending with 7-round mock interviews and 100% placement assurance.
Daily Topic Mastery (>98%), AI Interviews & Exit Benchmark (≥95%)
Our goal is never simple rote memorization. MockAttempt is engineered to forge elite software professionals with world-class logical skills, problem-solving skills, and architectural communication clarity. For each topic and before graduating, students must clear our rigorous multi-dimensional evaluation pipeline:
For every single day's lesson/topic, candidates must score over 98% in proctored mock assessments. Students have access to 100+ dedicated Mock Tests per topic to practice edge cases, algorithmic variations, and syntax nuances until full clarity is proven.
Students undergo daily interactive, topic-specific AI voice and coding technical interviews. The AI strictly focuses on that day's concept, posing Socratic follow-ups to evaluate depth, articulation, and reasoning speed with a >98% score benchmark.
At the conclusion of the program, students must achieve a minimum of 95% in the comprehensive Final AI Technical Interview and Grand Mock Assessment to certify completion and qualify for corporate recruitment drives and the 100% Fees Refund Guarantee*.
Students receive unlimited opportunities to re-attempt mock tests and AI interviews. The goal is complete skill mastery: instant detailed feedback, complexity analysis, and AI hints help you learn from mistakes and build solid confidence.
Comprehensive Curriculum & Modules (85 Modules)
Hands-on engineering syllabus with real project implementations, daily topic mock tests & live AI mock interviews.
Hardware, OS, processes, threads, memory, networking, client-server models, web architectures, and developer tooling.
Python syntax, dynamic typing, primitive and collection types, operators, conditionals, loops, functions, and string formatting.
Object-oriented design, inheritance, magic methods, exception handling, custom exceptions, modules, and file handling.
Comprehensions, functional programming, iterators, generators, decorators, type hints, dataclasses, and async programming.
Version control fundamentals, branching, merging, rebasing, pull requests, code reviews, and GitHub Actions.
Time/space complexity, arrays, strings, linked lists, stacks, queues, trees, heaps, graphs, recursion, and interview problems.
RDBMS principles, CRUD operations, joins, subqueries, CTEs, window functions, database design, indexing, and transactions.
PostgreSQL installation, schemas, JSONB, full-text search, CTEs, triggers, and AI conversation/metadata storage.
HTML5 semantic markup, forms, modern CSS, Flexbox, Grid, responsive layouts, and AI application UI interfaces.
JavaScript fundamentals, ES6+ features, DOM manipulation, asynchronous promises, Fetch API, and streaming responses.
TypeScript types, interfaces, generics, utility types, and strict domain modeling for AI applications.
React 19 component architecture, hooks, routing, global state, API integration, and production AI application UIs.
FastAPI architecture, ASGI, Pydantic validation, dependency injection, middleware, streaming responses, and WebSockets.
Django MVT, ORM QuerySets, migrations, admin portal, serializers, ViewSets, and enterprise admin systems.
REST principles, HTTP status codes, headers, pagination, filtering, versioning, rate limiting, and AI-specific API design.
User registration, password hashing, JWT access/refresh token rotation, role-based access control (RBAC), and OAuth2.
AI taxonomy, machine learning paradigms, end-to-end ML workflows, classical algorithms, and evaluation metrics.
Linear algebra, matrix operations, probability, Bayes theorem, statistics, calculus, and gradient descent for AI systems.
Multidimensional arrays, vectorization, Pandas Series/DataFrames, data cleaning, aggregation, and AI data preprocessing.
Preprocessing, supervised models, hyperparameter tuning, model evaluation, and practical candidate skill classification.
Neural networks, perceptrons, activation functions, loss functions, backpropagation, and PyTorch with CUDA acceleration.
Text preprocessing, tokenization, stemming/lemmatization, POS tagging, NER, TF-IDF, word embeddings, and text classification.
Transformer architecture, encoder-decoder, self-attention, multi-head attention, positional encodings, and model families.
LLM pretraining, tokenization, context windows, reasoning, capabilities, failure modes, hallucinations, and open-weight models.
API keys, authentication, inference parameters, streaming, retries, rate limiting, cost tracking, and reusable LLM service design.
Instructions, context, zero/few-shot prompting, Chain-of-Thought, structured JSON outputs, Pydantic validation, and security.
Dense vector embeddings, semantic similarity, distance metrics, chunking strategies, and end-to-end vector pipelines.
Vector indexing, pgvector, Chroma, Qdrant, FAISS, vector CRUD, metadata filtering, hybrid search, and HNSW graph indexing.
RAG lifecycle, document parsing, fixed vs semantic chunking, vector retrieval, context injection, and citation attribution.
Query rewriting, multi-query expansion, HyDE, cross-encoder reranking, context compression, and Ragas evaluation.
PDF ingestion, OCR for scanned documents, table detection, form extraction, and multi-page document processing pipelines.
Autonomous agents vs chatbots, planning, reasoning, tool use, agent state, execution loops, and self-correction.
Tool definitions, JSON schemas, function execution, argument validation, custom tool building, and sandboxing.
Agent workflows, ReAct pattern, router pattern, supervisor pattern, and multi-agent team orchestration.
MCP architecture, Client-Server model, tools, resources, prompts, custom server implementation, and security.
Short-term, conversation, long-term, semantic, and episodic memory with multi-tier storage in Redis, Postgres, and vector stores.
Text, image, audio, video, vision understanding, OCR, Speech-to-Text (STT), Text-to-Speech (TTS), and multimodal interview systems.
Code generation, explanation, refactoring, bug detection, AST chunking, symbol-aware indexing, and repository RAG.
Benchmark methodologies, human evaluation, LLM-as-a-judge, Ragas metrics for RAG, and agent evaluation frameworks.
Token usage telemetry, inference latency, cost tracking, distributed tracing, span trees, and production monitoring.
OWASP Top 10 for LLMs, prompt injection, jailbreaks, data leakage, PII masking, tool execution guardrails, and sandboxing.
AI safety, privacy, bias mitigation, fairness, transparency, explainability, human-in-the-loop oversight, and governance.
Token economics, prompt optimization, semantic caching with Redis, intelligent model routing, and batch processing.
Production architecture design, React, API Gateway, FastAPI, vector stores, Redis, PostgreSQL, Kafka, and fault tolerance.
Conversation buffers, session management, rate limiting, semantic caching, agent state, and distributed locks.
Asynchronous processing, document extraction, embedding generation, task queues, workers, and Celery Beat.
Service boundaries (Auth, Document, RAG, Agent, Eval), inter-service REST/gRPC/Kafka communication, and resilience.
Event-driven AI pipelines, document processing events, evaluation events, Kafka producers, consumer groups, and scaling.
Enterprise Linux administration, filesystem, permissions, processes, networking, SSH bastions, and Bash automation.
Containers, Docker CLI, multi-stage Dockerfiles for Python/AI, Docker Compose orchestration, and security hardening.
Kubernetes cluster architecture, control plane, pods, deployments, services, ingress, autoscaling, and operations.
DevOps lifecycle, continuous integration, continuous delivery, automation, infrastructure as code, and monitoring.
Workflows, jobs, steps, runners, automated testing, security scanning, container builds, and deployment to Amazon ECR/EKS.
Jenkins architecture, controller, agents, declarative pipelines, Jenkinsfile, plugins, credentials, and webhooks.
Infrastructure as Code, HCL, providers, resources, variables, modules, state management, and provisioning AWS infrastructure.
Cloud computing models, AWS global infrastructure (Regions, AZs), IAM users, groups, roles, policies, and least privilege.
EC2 instances, AMIs, EBS storage, security groups, Elastic IPs, application deployment, Nginx reverse proxy, and SSL.
VPC design, CIDR blocks, public/private subnets, route tables, IGW, NAT Gateways, security groups, NACLs, and endpoints.
S3 buckets, objects, storage classes, lifecycle policies, encryption, presigned URLs, and storing training documents/resumes.
Managed PostgreSQL/MySQL, automated backups, multi-AZ, read replicas, and storing users, conversations, and AI metadata.
NoSQL tables, partition/sort keys, query/scan operations, GSI, TTL, streams, and storing agent state and activity logs.
Serverless architecture, Python runtimes, handlers, layers, triggers, cold starts, and serverless document processing.
REST/HTTP APIs, routes, integrations, authentication with Cognito/JWT, throttling, CORS, and deployment stages.
Standard and FIFO queues, visibility timeouts, dead-letter queues, retries, and asynchronous AI document processing.
SNS topics, fan-out architectures, EventBridge rules, event buses, and asynchronous AI event-driven workflows.
Elastic Container Registry, EKS cluster setup, node groups, IAM roles for service accounts, and scaling AI microservices.
Managed foundation models (Claude, Llama, Titan), Bedrock Knowledge Bases, Bedrock Agents, and Python Boto3 integration.
Metrics, log groups, alarms, operational dashboards, CloudTrail audit trails, and monitoring token usage and latency.
MLOps lifecycle, data pipelines, model registries, experiment tracking, model deployment, and continuous monitoring.
Prompt, model, and dataset versioning, golden test datasets, prompt regression testing, and quality/cost/latency monitoring.
End-to-end AI CI/CD pipeline, automated prompt/RAG/agent test suites, safety tests, and automated model promotions.
Security in CI/CD, SAST, DAST, secret scanning, container scanning, AI threat modeling, and prompt injection testing.
System design for complete AI platforms: users, frontend, API gateway, FastAPI, RAG, agents, vector DBs, and scalability.
Async processing, connection pooling, RAG chunk/rerank optimization, token reduction, streaming, and autoscaling.
Deep-dive implementation review of 8 industry production projects from AI Chat and RAG to Autonomous Agents and MCP platforms.
MockAttempt AI Career Intelligence Platform: candidate profiling, skill analysis, RAG curriculum assistant, and AI interviewer.
Unit/integration testing, API testing, prompt behavior testing, structured output testing, and golden dataset regression.
Evaluation metrics for student outputs, agent multi-step execution, tool selection, RAG faithfulness, and user feedback.
Responsible AI, privacy, data protection, PII handling, access control, audit logs, model governance, and compliance.
Comprehensive technical interview prep across Python, FastAPI/Django, React, GenAI, Transformers, RAG, Agents, AWS, and DevOps.
ATS optimization, quantified achievement statements, GitHub repository standards, live AI demos, and portfolio website.
5-Level multi-modal assessment architecture: Knowledge MCQs, Technical Analysis, Practical Tasks, Project Reviews, and AI Vivas.
Distinguishing course completion from demonstrated skill evidence, 7 competency dimensions, and verified skill passport.
16-stage progressive verification pipeline, mandatory gate clearances, and placement assurance activation.
Course hierarchy (Course -> Module -> Topic -> Lesson), multi-modal learning assets, and integrated verification gates.
Portfolio Capstone & Production Projects (9 Projects)
Build and defend production repositories featuring clean architecture, CI/CD, and benchmark testing.
Project 1 — AI Chat Application with Token Analytics
Hands-on architectural implementation built with industry clean-code standards.
Project 2 — Enterprise RAG Document Assistant
Hands-on architectural implementation built with industry clean-code standards.
Project 3 — AI Interactive Technical Interview Platform
Hands-on architectural implementation built with industry clean-code standards.
Project 4 — AI Codebase Assistant & Repository RAG
Hands-on architectural implementation built with industry clean-code standards.
Project 5 — Autonomous AI Recruitment & Candidate Matching Agent
Hands-on architectural implementation built with industry clean-code standards.
Project 6 — Multi-Agent Autonomous Research Platform
Hands-on architectural implementation built with industry clean-code standards.
Project 7 — AI Customer Support & Ticket Resolution Platform
Hands-on architectural implementation built with industry clean-code standards.
Project 8 — Enterprise AI Knowledge Platform with RBAC & MCP
Hands-on architectural implementation built with industry clean-code standards.
Final Capstone — MockAttempt AI Career Intelligence Platform
Hands-on architectural implementation built with industry clean-code standards.
Progressive Certification Ladder (5 Verified Levels)
Candidates earn industry shareable digital credentials as they clear milestone coding rounds and architecture reviews.
Certified Python & AI Foundation Engineer
Phase 1–2 CertifiedVerified competency in foundational Python 3.12 programming, object-oriented design, algorithmic problem solving, and relational query syntax.
Certified Full Stack AI Backend Developer
Phase 3–4 CertifiedVerified competency in building secure, asynchronous streaming REST backends with FastAPI and React 19, JWT/OAuth2 authentication, and full-stack integration.
Certified RAG & Vector Systems Specialist
Phase 5–7 CertifiedVerified competency in PyTorch, Transformer attention mechanics, dense/sparse vector retrieval with pgvector/Qdrant, and production RAG pipeline architecture.
Certified Autonomous AI Agents & MCP Architect
Phase 8 CertifiedVerified competency in orchestrating autonomous multi-agent state machines, tool calling schemas, custom Model Context Protocol (MCP) servers, and episodic memory.
Certified GenAI Full Stack & LLMOps Cloud Master
Phase 9–10 Placement ReadyThe ultimate industry credential: end-to-end mastery across AWS Bedrock, Kubernetes EKS, Terraform IaC, LLMOps observability, AI security guardrails, and enterprise capstone deployment.
🎯 Target Audience & Career Persona
Software Developers, Backend Engineers, Data Scientists, and Ambitious Graduates aiming for high-impact Generative AI Engineer, LLM Application Developer, and AI Full Stack roles.
📋 Academic & Technical Prerequisites
Basic programming orientation and high-school math. The curriculum begins from computer and software fundamentals and builds systematically to production AI architectures.
MockAttempt 100% Placement & Refund Guarantee vs Other Models
See why our fixed-fee model protects your career and saves you up to ₹2.5 Lakhs compared to predatory Income Share Agreements (ISAs).
| Evaluation Metric | ✨ MockAttempt Placement Track | Pay After Placement (ISA) | Traditional Bootcamps |
|---|---|---|---|
| Total Tuition Cost | Flat ₹49,999 (All-Inclusive) | ₹1,50,000 – ₹3,00,000+ | ₹75,000 – ₹1,50,000 |
| Post-Job Salary Deductions | 0% Taken (You keep 100% of salary) | 15%–20% monthly for 24–36 mos | 0% (but high upfront fee) |
| 100% Money-Back Guarantee | ✓ 100% Bank Refund in 6 Months | ❌ Registration fees non-refundable | ❌ Strictly No Refunds |
| Legal Placement Covenant | ✓ Written Signed Agreement (Day 1) | Strict legal debt bond on student | "Assistance only" (no legal claim) |
| Topic Mock Tests & Defenses | 3,800+ Mock Tests (20 per topic) | 10–15 generic quizzes | Basic theoretical assignments |
| Interview Practice Mode | 1-on-1 AI Viva STT + Stealth Teleprompter | 2–3 peer mock rounds | None / Static QA PDFs |
Frequently Asked Questions & Placement Refund Policy
Everything you need to know about the 6-Month 100% Fees Refund Policy, curriculum structure, and placement drives.
Does MockAttempt offer a 100% placement guarantee with a refund for the Generative AI & LLM Course? ↓
Yes. MockAttempt provides a legally backed 100% Fees Refund Placement Guarantee under Section 10A of the Information Technology Act. If you complete the Generative AI, LLM & Agentic AI Full Stack Engineering Course, maintain 90% attendance, achieve >98% aggregate in daily topic mock tests (with unlimited retakes), and pass the exit capstone defense, but do not secure a qualifying GenAI Developer, LLM Engineer, or AI Full Stack role within 6 Months (180 days) post-graduation, 100% of your paid course tuition fees (₹49,999) is refunded directly to your bank account within 14 business days with zero deductions.
What is the fee for the Generative AI & LLM Developer Course with placement guarantee? ↓
The program has a transparent all-inclusive fixed fee of ₹49,999 (No hidden charges). No-cost 6-month EMI is available starting at ₹8,333/month. Unlike other institutes charging ₹1,40,000 to ₹1,70,000 or ISA bootcamps that deduct 15% to 20% of your salary for 2-3 years (costing ₹1.5L to ₹3.0L), MockAttempt charges only the fixed ₹49,999 fee backed by our contractual 100% money-back guarantee.
How does MockAttempt's 100% Placement & Refund Guarantee compare to Pay After Placement (ISA)? ↓
Pay After Placement (ISA) bootcamps typically take 15-20% of your monthly salary for 24 to 36 months, costing up to ₹3,00,000 in total, and often push candidates into low-paying non-AI tech support or data annotation roles just to trigger the payment clause. MockAttempt offers a flat ₹49,999 fee with zero salary cuts after you get hired, guarantees placement specifically in qualifying GenAI/LLM/Software engineering roles, and issues a 100% bank refund if you remain unplaced within 6 months post-training.
What GenAI frameworks, vector databases, and cloud platforms are covered in this program? ↓
You will master Python 3.12, FastAPI asynchronous backends, React 19 & TypeScript, PostgreSQL with pgvector, Redis semantic caching, PyTorch, Transformer architectures, Large Language Models (OpenAI, Claude, Llama 3, Mistral), Prompt Engineering, Advanced RAG (hybrid search, HyDE, cross-encoder rerankers, citation groundings), Autonomous AI Agents (ReAct, Supervisor, Router workflows), Model Context Protocol (MCP) clients/servers, Multimodal AI, Ragas/TruLens evaluation, LLM Security (OWASP Top 10 for LLMs), LLMOps, Celery/Kafka, AWS Bedrock, Amazon EKS, Docker, Kubernetes, and Terraform across 36 weeks and 540 practical hours.
How many topic mock tests, AI viva defenses, and capstone projects are included? ↓
The course includes over 6,000+ calibrated mock tests across 85 modules and 200+ topics (at least 20 tests per topic), 8 enterprise production capstones (Enterprise RAG with Citations, Multi-Agent Research System, Financial Document AI OCR, Multimodal Assistant, MCP Development Toolchain), live 1-on-1 AI voice viva technical defense interviews, and the Windows Desktop Stealth Teleprompter HUD for interview performance.
Can college freshers or software developers without prior AI experience join this placement track? ↓
Yes. Freshers (B.Tech, BCA, MCA, B.Sc), backend engineers, full stack developers, and data analysts with career gaps are fully supported. The curriculum begins with programming fundamentals, Python 3.12, and mathematics for AI before methodically progressing to modern LLMs, autonomous agents, and enterprise cloud deployments, with 150+ corporate hiring partners actively hiring our graduates.
Backed by our 100% money-back guarantee: if not placed within 6 months post-training, your complete tuition is refunded.
Enroll with 6-Month Placement Assurance
Speak with a technical advisor and receive your complete syllabus roadmap and legal guarantee agreement.
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