World-Class Job-Ready Curriculum · For Fresh Graduates
APEX — AI · ML · Cloud & Cyber Security Engineering Program
A 16-week, project-driven mastery track that transforms fresh graduates into industry-ready engineers — Python, Machine Learning, Generative & Agentic AI, Data Science, Multi-Cloud (AWS), DevOps and Cyber Security, taught the way real teams build and ship.
★ World's Best Curriculum100% Placement GuaranteeTaught by Industry Experts
PythonMachine LearningGenerative AIAgentic AIData ScienceAWS Multi-CloudDevOpsCyber Security
16wksIntensive hands-on training across 4 phases
4+Real-time, resume-ready industry projects
8In-demand technology domains, one track
100%Placement guarantee with relentless interview prep
Cloud Soft Solutions is a Hyderabad-based IT training and placement institute helping graduates and early-career professionals break into high-growth technology careers. Headquartered in Ameerpet — India's largest IT-training hub — we deliver a world-class, industry-aligned curriculum in cloud, AI, data and DevOps, taught by working engineers and backed by a 100% placement guarantee.
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Industry-First Curriculum
Course content modelled on real engineering teams — the same tools, workflows and trade-offs you will meet on the job.
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Project-Driven Learning
You don't just watch — you build. Every phase ends in a working deliverable that goes straight into your portfolio.
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100% Placement Guarantee
Resume engineering, LinkedIn optimisation, unlimited mock interviews and active placement drives — backed until you are placed.
Why graduates choose this program
A world-class, industry-aligned curriculum. Built around the exact skills companies are hiring for right now — AI, ML, cloud, DevOps and security — and refreshed continuously.
One track, eight high-demand domains. You graduate as a full-stack AI-Cloud engineer who can build, deploy and secure intelligent applications end-to-end.
Taught by working engineers. Mentors with real production experience in cloud, AI and DevOps — not just slides, but war stories and best practices.
Real-time scenarios in every module. Each week is anchored to a problem a real company faces, so you learn the "why" behind the "how".
A portfolio that gets interviews. Four progressively harder projects culminate in a production-grade, secure AI cloud platform you can demo with confidence.
Career support that doesn't stop at the certificate. Aptitude, communication, DSA refreshers, system-design basics and interview practice are built in.
Who this program is for
Fresh graduates (BE / B.Tech / MCA / B.Sc / M.Sc / BCA / degree) and early-career professionals who want a single, structured path into AI, cloud and DevOps roles. No prior coding experience is assumed — we start from fundamentals and ramp you to job-ready depth.
Program Overview
The skills employers are actually hiring for.
The market has shifted. Companies no longer want narrow specialists who can only train a model or only write Terraform. They want engineers who can take an idea — an AI assistant, a data product, a smart automation — and carry it all the way to a secure, monitored, production deployment. This program is engineered around exactly that profile.
Program at a glance
Duration
4 months · 16 weeks · 4 structured phases
Format
Instructor-led, hands-on labs every session · classroom & live-online options
Most of your time is spent writing code, breaking things, debugging and shipping — on your own machine and on the cloud.
20
Guided Mentoring
Live walkthroughs, code reviews and architecture discussions with engineers who have built real systems.
10
Concepts & Theory
Just enough foundational theory to understand the "why" — never theory for its own sake.
The learning journey — four phases
Phase
Focus
What you walk away able to do
Month 1 Weeks 1–4
Python, Cloud & AI Foundations
Write solid Python, deploy your first app to AWS, and build your very first GenAI-powered, agentic application — all in month one.
Month 2 Weeks 5–8
Machine Learning, GenAI & Agentic AI
Train and deploy ML models, engineer prompts, and build Retrieval-Augmented and agentic AI applications.
Month 3 Weeks 9–12
Multi-Cloud (AWS) & DevOps
Provision cloud infrastructure as code and ship containerised apps through automated CI/CD pipelines.
Month 4 Weeks 13–16
Cyber Security, DevSecOps & Capstone
Secure cloud and AI applications, embed security into pipelines, and ship a production-grade capstone.
The thread that ties it together
Each project deliberately builds on the last. You analyse data (Project 1), make it intelligent with GenAI and agents (Project 2), ship it to the cloud with full DevOps automation (Project 3), and finally harden the whole thing into a secure, production-grade platform (Capstone). One coherent story — a portfolio that proves you can do the whole job.
Curriculum
16 weeks. Four phases. One job-ready engineer.
Month 1 — Python, Cloud & AI Foundations
01
Foundation Phase · Weeks 1–4
Python, your first cloud deploy & your first AI app
No waiting months to touch the exciting tech. From week one you write Python and launch resources on AWS — and by week four you've built your first GenAI-powered, agentic application on real data.
Leads into Project 1
WEEK 01
Python Essentials & Your Cloud Launchpad
Programming fundamentals — plus your very first step into the cloud.
Core Topics
Environment setup: Python, pip, virtual environments, VS Code & Jupyter
Variables, data types, operators, input/output and string formatting
Control flow: conditionals, for/while loops, comprehensions
Core data structures: lists, tuples, sets, dictionaries
Cloud fundamentals: regions & key AWS services, creating your AWS account & navigating the console
Hands-On Labs
Build a command-line calculator & number-game
Text-processing utility (word counts, cleaning)
Create your AWS Free-Tier account & launch your first cloud resource
Real-Time Scenario
Build a CLI Expense Tracker and set up the AWS cloud account you'll deploy your projects to — writing real code and stepping into the cloud from day one.
WEEK 02
Advanced Python, Git & AWS Core Services
OOP and clean code — and your app running on a real cloud server.
Model a domain with classes & write pytest unit tests
Initialise a Git repo, branch & push to GitHub
Deploy a Python app to a live AWS EC2 instance
Real-Time Scenario
Build a Live Weather & News service, version-control it on GitHub, and deploy it to a real EC2 server on AWS — your first application running in the cloud.
WEEK 03
Data with Pandas & SQL + Your First GenAI App
Wrangle real, messy data — and start building with large language models.
Reading data from CSV, Excel, JSON, APIs and databases
SQL essentials: SELECT, WHERE, JOIN, GROUP BY; Python ↔ database
The GenAI landscape: LLMs — GPT, Claude, Llama, Gemini — and how they work
Building with GenAI: calling LLM APIs from Python, prompt-engineering basics & structured output
Hands-On Labs
Clean a messy retail dataset & join multiple tables
Query a SQL database from Python
Build a GenAI-powered text assistant using an LLM API
Real-Time Scenario
Turn a raw e-commerce sales export into clean, analysis-ready data — then add a GenAI layer that auto-summarises the key insights in plain English.
WEEK 04
EDA, Visualisation & Your First AI Agent
Tell the story in the data — and build an autonomous AI assistant.
Core Topics
Visualisation with Matplotlib, Seaborn & Plotly
Exploratory Data Analysis (EDA) — a repeatable workflow
Statistics for data science: distributions, hypothesis testing, correlation
Data storytelling & building interactive apps with Streamlit
Agentic AI intro: what an AI agent is — tools, actions & the ReAct pattern
Build an agent: a simple tool-using AI assistant on top of an LLM
Hands-On Labs
Full EDA on a real dataset with a visual report
Ship an interactive Streamlit dashboard
Build your first AI agent that uses a tool (search / calculator)
Real-Time Scenario
Project 1 kickoff: build an interactive Sales-Insights Dashboard in Streamlit with a built-in AI assistant that answers questions about the data in plain English.
Month 2 — Machine Learning, GenAI & Agentic AI
02
Intelligence Phase · Weeks 5–8
Make your applications think
You've already built your first GenAI app and AI agent — now go deep: classical and advanced machine learning, deep learning, and production-grade Retrieval-Augmented and multi-agent systems.
Build a prompt-engineered content & code assistant that produces reliable, structured responses — your first real taste of building with generative AI, not just using it.
WEEK 08
Agentic AI, RAG & LLM Application Engineering
Retrieval, tools, memory and multi-agent orchestration.
Chat UIs with Streamlit / Gradio over a FastAPI backend
Hands-On Labs
Build a RAG pipeline over your own documents
Create a tool-using agent (search + calculator)
Orchestrate two agents on one task
Real-Time Scenario
Project 2 kickoff: build an Enterprise Document Q&A Assistant (RAG) plus an autonomous support agent that can look things up and take actions — the intelligence core of your second project.
Month 3 — Multi-Cloud (AWS) & DevOps
03
Production Phase · Weeks 9–12
Ship it — at scale, in the cloud
You launched your first app on AWS in month one — now master the cloud: infrastructure as code, containers, Kubernetes and fully automated delivery pipelines that ship at scale.
Leads into Project 3
WEEK 09
Cloud Computing & AWS Core Services
Compute, storage and networking — the AWS foundation.
Containerise your ML/AI application with Docker and build an automated CI pipeline that tests and builds it on every commit — the foundation of reliable delivery.
Project 3 kickoff: stand up an end-to-end pipeline — CI/CD → Docker → Kubernetes (EKS) on Terraform-provisioned infrastructure, with Prometheus & Grafana monitoring.
Month 4 — Cyber Security, DevSecOps & Capstone
04
Mastery Phase · Weeks 13–16
Secure it, prove it, get hired
Harden everything you've built with security and DevSecOps, then bring all four phases together into a production-grade capstone — and get interview-ready.
Capstone + Placement
WEEK 13
Cyber Security Foundations
Threats, cryptography, identity and the OWASP Top 10.
Core Topics
Security principles: CIA triad, threats, vulnerabilities & risk
OWASP Top 10 web vulnerabilities: SQLi, XSS, CSRF and more
Security tooling: Nmap, Wireshark, Burp Suite; Linux hardening
Hands-On Labs
Scan a network & read the results
Exploit & then fix an XSS / SQLi demo app
Harden a Linux server checklist
Real-Time Scenario
Perform a vulnerability assessment of a sample web application, document the findings, and remediate them — thinking like both an attacker and a defender.
WEEK 14
Cloud Security & DevSecOps
Shift security left — into the cloud, containers and pipeline.
Core Topics
AWS security: IAM least privilege, KMS encryption, GuardDuty, Security Hub, WAF, secrets management
Securing AI/LLM apps: prompt injection, data leakage, OWASP LLM Top 10
Hands-On Labs
Add image scanning (Trivy) to the pipeline
Lock down IAM & encrypt with KMS
Add a secrets-scan & SAST gate to CI
Real-Time Scenario
Capstone kickoff: add a security gate to your DevOps pipeline and harden your cloud infrastructure — the DevSecOps layer that turns a working app into a production-grade one.
Professional documentation: README, architecture diagrams, runbooks
Debugging & troubleshooting across the stack
Hands-On Labs
Integrate model + agent + cloud + pipeline
Peer & mentor code review rounds
Write production-grade documentation
Real-Time Scenario
Run real capstone build sprints — designing, integrating and reviewing the full platform under mentorship, exactly like a delivery team working toward a release.
Demo it, package it, and walk into interviews prepared.
Core Topics
Capstone deployment, demo & presentation
GitHub portfolio polishing & a technical project write-up / blog
ATS-optimised resume building & LinkedIn optimisation
Mock interviews — technical & HR rounds
DSA refresher, coding-challenge practice & system-design basics
Aptitude, communication & group-discussion practice
Hands-On Labs
Final demo day presentation
Recorded mock-interview rounds with feedback
Polish resume, GitHub & LinkedIn
Real-Time Scenario
Placement: present your capstone on Demo Day and run the mock-interview gauntlet, then enter active placement drives with a portfolio that proves you can do the job.
Real-Time Projects
Four projects. One job-winning portfolio.
Every phase produces a real, working project — and each one deliberately builds on the last. By the end you don't have four disconnected demos; you have one coherent, production-grade platform that proves you can take an idea from data to a secure cloud deployment.
01
Phase 1 · Data & ML
InsightHub — Intelligent Data Analytics Platform
Months 1–2
Business scenario: A growing e-commerce company is losing customers and can't see why. They need a platform that cleans their messy sales data, surfaces insights through an interactive dashboard, and predicts which customers are likely to churn — so the team can act before revenue walks out the door.
What you build
A data pipeline that ingests and cleans raw sales data
An interactive Streamlit analytics dashboard (revenue by region, product, time)
A churn-prediction ML model with proper evaluation
A built-in GenAI assistant that answers data questions in plain English
Skills demonstrated
Data wrangling & EDA on real data
Supervised ML & model evaluation
Data storytelling & dashboarding
Turning analysis into decisions
PythonPandasNumPyscikit-learnPlotlyStreamlitSQLLLM API
02
Phase 2 · GenAI & Agents
AskCloud — Agentic AI Enterprise Assistant
Month 2
Business scenario: An organisation's staff waste hours hunting through internal documents and doing repetitive lookups. They want an AI assistant that answers questions from their own knowledge base and can take actions — search records, summarise, draft replies — reliably and without hallucinating.
What you build
A RAG pipeline: document ingestion, chunking, embeddings & a vector store
A multi-tool AI agent that can search, calculate and call functions
Business scenario: The AI assistant works on a laptop — but the business needs it running reliably for thousands of users, deploying automatically on every code change, and scaling on demand. You build the production delivery platform that makes that possible.
What you build
Containerise the application with Docker
An automated CI/CD pipeline (GitHub Actions / Jenkins) pushing to ECR
Deployment to Kubernetes on Amazon EKS (with ECS option)
AWS infrastructure provisioned with Terraform; Prometheus + Grafana monitoring
Business scenario: Deliver a complete, production-grade, secure AI cloud platform — an intelligent customer-support & analytics product that integrates everything you've built and meets the security bar real companies require before going live. This is your portfolio centrepiece.
Cloud hardening: IAM least privilege, KMS encryption, WAF, secrets management
LLM security guardrails, monitoring, logging & an incident-response plan
Skills demonstrated
Full-stack AI-Cloud engineering
DevSecOps & cloud security
Architecture, integration & optimisation
Production readiness end-to-end
Full AI/ML stackAgentic AIAWS (multi-service)KubernetesTerraformTrivy / SnykSonarQubeKMS / WAF / IAM
Plus — mini-projects every single week
Beyond the four flagship projects, each week ships a hands-on lab and a real-time scenario task. You graduate with a GitHub profile full of working code — not an empty repository.
Tools & Technologies
The complete industry toolkit.
You'll work with the same tools professional engineering teams use every day — across programming, data, AI, cloud, DevOps and security.
Technical skill gets you in the door — interview readiness gets you the offer. Career support is woven through the program and intensifies in the final phase, backed by our placement guarantee.
Our Commitment to You
100% Placement Guarantee
Complete the program and meet the assessment criteria, and we back you with unlimited interview opportunities, continuous profile sharing with hiring partners and dedicated support — until you are placed.
100%
Portfolio engineering. Polish your GitHub, write a technical project blog, and package your capstone so recruiters can see real, working proof of your skills.
ATS-optimised resume & LinkedIn. A recruiter-friendly resume mapped to target roles, plus a LinkedIn profile that gets you found.
Aptitude & communication. Quantitative, logical and verbal aptitude practice, plus group discussions and spoken-English confidence.
DSA & system-design refresher. Coding-challenge practice and the system-design fundamentals that technical rounds expect.
Mock interviews with feedback. Repeated technical and HR mock rounds with engineers — recorded, reviewed and improved until you're sharp.
Active placement drives. Profile sharing with hiring partners, interview scheduling and ongoing support through the placement process.
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In-Demand Salaries
AI, cloud and DevOps are among the highest-paid entry tracks in the Indian IT market — and demand keeps rising.
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Growing Job Market
GenAI and cloud roles are expanding rapidly across product companies, startups and services firms.
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Multiple Role Options
One program qualifies you for ML, GenAI, data, cloud, DevOps and security roles — you choose your direction.
Assessment & Certification
Progress is measured through weekly hands-on tasks, phase assessments, project reviews and mock interviews. On successful completion you receive a Cloud Soft Solutions Course Completion Certificate, a job-ready project portfolio, and continued placement support.
FAQ
Questions, answered.
What is the APEX program and who is it for?
APEX is a 16-week, project-driven engineering program that takes fresh graduates and early-career professionals to job-ready in AI, ML, Generative & Agentic AI, Data Science, Multi-Cloud (AWS), DevOps and Cyber Security. It's designed for graduates of any stream (BE/B.Tech/MCA/B.Sc/M.Sc/BCA/degree) — no prior coding experience is assumed.
How long is the program and what is the format?
16 weeks across 4 structured phases (4 months). It's instructor-led with hands-on labs every session, available as classroom batches in Ameerpet, Hyderabad and live-online batches. Expect daily sessions plus guided practice, project sprints and weekend doubt-clearing.
Do I need prior coding experience?
No. APEX starts from Python fundamentals and ramps you to job-ready depth. You only need basic computer literacy and logical aptitude, with graduation in any stream.
What is the 100% Placement Guarantee?
Complete the program and meet the assessment criteria, and Cloud Soft Solutions backs you with unlimited interview opportunities, continuous profile sharing with hiring partners and dedicated support until you are placed — including resume engineering, LinkedIn optimisation and unlimited mock interviews.
What projects will I build?
Four real, resume-ready projects that build on each other: InsightHub (intelligent data analytics platform), AskCloud (agentic AI enterprise assistant), DeployX (cloud-native DevOps pipeline on AWS) and the SecureAI capstone (end-to-end secure AI cloud platform). Plus a hands-on lab and real-time scenario every single week.
Which job roles can I target after APEX?
AI/ML Engineer, Generative AI Engineer, Data Scientist/Analyst, Cloud Engineer (AWS), DevOps Engineer, Cloud Security/DevSecOps Engineer, Python Developer and MLOps/Site Reliability Engineer.
Is the program online or classroom?
Both. You can attend classroom batches at our Ameerpet campus or join live, instructor-led online batches — whichever suits you. Course materials, recordings and doubt-clearing are included.
What certifications does APEX prepare me for?
The cloud and DevOps phases map directly to industry certifications — AWS Certified Cloud Practitioner and AWS Certified Solutions Architect – Associate, plus foundational readiness for container/Kubernetes and security credentials — alongside your Cloud Soft Solutions course completion certificate.
Sixteen weeks. A world-class curriculum across eight in-demand domains. Four real projects. A 100% placement guarantee. One job-ready engineer — you. Seats are limited and batches fill quickly.