About the Institute
Built by practitioners. Trusted by employers.
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 |
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| Format | Instructor-led, hands-on labs every session · classroom & live-online options |
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| Effort | Daily sessions + guided practice + project sprints + weekend doubt-clearing |
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| Projects | 4+ real-time projects (one per phase) building toward a capstone platform |
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| Assessment | Weekly hands-on tasks, phase assessments, project reviews, mock interviews |
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| Prerequisites | Basic computer literacy and logical aptitude · graduation in any stream |
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| Outcome | Job-ready AI-Cloud engineer + portfolio + course completion certificate + 100% placement guarantee |
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How you'll learn — the 70/20/10 model
70
Hands-On Building
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 |
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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.
WEEK
01Python 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
- Functions, scope, default/keyword args, *args & **kwargs, lambda
- 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
02Advanced Python, Git & AWS Core Services
OOP and clean code — and your app running on a real cloud server.
Core Topics
- OOP: classes, objects, inheritance, polymorphism, encapsulation, dunder methods
- Modules, packages & project structure; file handling (CSV/JSON), exceptions & logging
- Iterators, generators, decorators, type hints
- Consuming REST APIs with requests; testing with pytest
- Version control: Git & GitHub — branches, commits, pull requests
- AWS core services: EC2, S3 (object storage) & IAM (users, roles & access)
Hands-On Labs
- 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
03Data with Pandas & SQL + Your First GenAI App
Wrangle real, messy data — and start building with large language models.
Core Topics
- NumPy: arrays, vectorisation, broadcasting, numerical operations
- Pandas: DataFrames, cleaning, filtering, grouping, merge/join, pivots
- 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
04EDA, 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.
WEEK
05Machine Learning Foundations — Supervised Learning
The ML lifecycle and the core algorithms, done properly.
Core Topics
- ML lifecycle, problem framing, train/validation/test split, cross-validation
- The scikit-learn workflow & pipelines
- Regression: linear, polynomial, regularisation (Ridge / Lasso)
- Classification: logistic regression, KNN, decision trees, SVM, Naïve Bayes
- Feature engineering: scaling, encoding, transformation
- Evaluation: accuracy, precision, recall, F1, ROC-AUC, RMSE, R²
Hands-On Labs
- Build a regression model end-to-end
- Train a multi-class classifier with a pipeline
- Compare models with proper metrics
Real-Time Scenario
Build a Customer Churn Predictor for a telecom company — identify which subscribers are about to leave so the business can act first.
WEEK
06Advanced ML, Tuning & Model Deployment
Ensembles, optimisation and serving models as real APIs.
Core Topics
- Ensembles: Random Forest, Gradient Boosting, XGBoost, LightGBM
- Unsupervised learning: K-Means, hierarchical clustering, PCA
- Hyperparameter tuning: GridSearch, RandomSearch, Optuna
- Imbalanced data & model interpretability with SHAP
- Model persistence (joblib) & serving with FastAPI
- MLOps intro: experiment tracking with MLflow
Hands-On Labs
- Boosted model + hyperparameter search
- Customer segmentation with clustering
- Wrap a model in a FastAPI prediction API
Real-Time Scenario
Build a Fraud-Detection model on imbalanced transaction data and expose it as a live FastAPI endpoint that returns a risk score in real time.
WEEK
07Deep Learning & Generative AI Foundations
Neural networks, transformers and the modern LLM toolkit.
Core Topics
- Neural-network fundamentals: layers, activations, backprop (intuition)
- Deep learning with TensorFlow/Keras & PyTorch basics
- CNNs for images & transfer learning; RNN/LSTM for sequences
- Transformers & attention — how LLMs actually work
- The GenAI landscape: GPT, Claude, Llama, Gemini & open models
- Prompt engineering: zero/few-shot, chain-of-thought, system prompts, structured output
- Using LLM APIs; tokens, context windows, temperature; embeddings & semantic search
Hands-On Labs
- Image classifier via transfer learning
- Call an LLM API with structured output
- Build a semantic-search demo with embeddings
Real-Time Scenario
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
08Agentic AI, RAG & LLM Application Engineering
Retrieval, tools, memory and multi-agent orchestration.
Core Topics
- Retrieval-Augmented Generation (RAG) architecture end-to-end
- Vector databases (FAISS, Chroma, Pinecone), chunking & embeddings
- Orchestration frameworks: LangChain & LlamaIndex
- AI agents: ReAct pattern, tool / function calling
- Multi-agent orchestration with LangGraph & CrewAI; planning & memory
- Guardrails, evaluation, hallucination mitigation, cost & latency optimisation
- 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.
WEEK
09Cloud Computing & AWS Core Services
Compute, storage and networking — the AWS foundation.
Core Topics
- Cloud concepts: IaaS / PaaS / SaaS, regions & AZs, shared responsibility
- IAM: users, groups, roles, policies, MFA & least privilege
- Compute: EC2, AMIs, instance types, security groups, Auto Scaling, Load Balancers
- Storage: S3 (versioning, lifecycle, static hosting), EBS, EFS
- Networking: VPC, subnets, route tables, IGW/NAT, SG vs NACL
- Multi-cloud awareness: mapping AWS ↔ Azure ↔ GCP services
Hands-On Labs
- Launch & secure an EC2 instance
- Host a static site on S3
- Build a custom VPC with public/private subnets
Real-Time Scenario
Deploy a scalable web application on EC2 behind a load balancer with auto-scaling — handling traffic spikes the way production systems must.
WEEK
10AWS Advanced & Cloud-Native Services
Serverless, managed databases, containers & AI on AWS.
Core Topics
- Serverless: Lambda, API Gateway, EventBridge, Step Functions
- Databases: RDS (PostgreSQL/MySQL), DynamoDB, ElastiCache
- Containers on AWS: ECR, ECS (Fargate), EKS overview
- Infrastructure as Code with CloudFormation
- Messaging & observability: SQS, SNS, CloudWatch, CloudTrail
- AI/ML on AWS: SageMaker overview & Amazon Bedrock (GenAI)
Hands-On Labs
- Build a serverless API with Lambda + API Gateway
- Provision an RDS database
- Push a container image to ECR
Real-Time Scenario
Deploy your Project-2 AI assistant to AWS using Lambda/ECS with S3 and API Gateway — taking a local prototype to a real cloud-hosted service.
WEEK
11DevOps Foundations — Linux, Git, CI/CD & Docker
The automation backbone of modern engineering.
Core Topics
- Linux administration: shell, file system, permissions, processes, networking
- Bash scripting & cron for automation
- Git advanced: branching strategies, PRs, merge vs rebase, GitHub/GitLab flow
- CI/CD concepts & pipelines with GitHub Actions and Jenkins
- Docker: images, Dockerfile, volumes, networks, Docker Compose, registries
- Artifact management & image best practices
Hands-On Labs
- Write bash scripts to automate tasks
- Containerise an app with a Dockerfile
- Build a CI pipeline that tests on every push
Real-Time Scenario
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.
WEEK
12DevOps Advanced — Kubernetes, Terraform & Observability
Orchestrate, provision and monitor like an SRE.
Core Topics
- Kubernetes: pods, deployments, services, ConfigMaps/Secrets, ingress, scaling
- Deploying to Amazon EKS; Helm basics
- Infrastructure as Code with Terraform: providers, resources, variables, state, modules (multi-cloud)
- Configuration management with Ansible (intro)
- GitOps with ArgoCD (intro)
- Monitoring & logging: Prometheus, Grafana, ELK / CloudWatch
Hands-On Labs
- Deploy a multi-service app to Kubernetes
- Provision AWS infra with Terraform modules
- Build a Grafana monitoring dashboard
Real-Time Scenario
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.
WEEK
13Cyber Security Foundations
Threats, cryptography, identity and the OWASP Top 10.
Core Topics
- Security principles: CIA triad, threats, vulnerabilities & risk
- Network security: firewalls, VPN, IDS/IPS, TLS/SSL, ports & protocols
- Cryptography: symmetric/asymmetric, hashing, PKI & certificates
- Identity & access: authentication, OAuth, JWT, SSO, MFA
- 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
14Cloud 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 containers & Kubernetes: image scanning, RBAC, network policies
- DevSecOps: shift-left, SAST / DAST / SCA, secrets & supply-chain scanning
- Security in CI/CD with Trivy, Snyk & SonarQube
- Compliance awareness: GDPR, ISO 27001, SOC 2; logging & incident response
- 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.
WEEK
15Capstone Build & System Integration
Bring all four phases together into one platform.
Core Topics
- End-to-end architecture design: AI/ML + agentic layer + cloud + DevOps + security
- Mentor-guided build sprints & structured code reviews
- Performance, scalability & cost optimisation
- 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.
WEEK
16Capstone Finalisation, Portfolio & Placement Readiness
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.
01Phase 1 · Data & ML
InsightHub — Intelligent Data Analytics Platform
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
02Phase 2 · GenAI & Agents
AskCloud — Agentic AI Enterprise Assistant
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
- A FastAPI backend with a Streamlit chat interface
- Guardrails, evaluation & cost/latency optimisation
Skills demonstrated
- Prompt engineering & LLM APIs
- RAG & vector search
- Agentic tool calling & orchestration
- Production LLM app engineering
LangChainLlamaIndexLangGraphFAISS / ChromaLLM APIsFastAPIStreamlit
03Phase 3 · Cloud & DevOps
DeployX — Cloud-Native DevOps Pipeline on AWS
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
Skills demonstrated
- Containerisation & orchestration
- Infrastructure as Code (Terraform)
- CI/CD automation & cloud deployment
- Observability & reliability
DockerKubernetesTerraformAWS EKS / ECSGitHub ActionsJenkinsPrometheusGrafana
★Phase 4 · Capstone
SecureAI — End-to-End Secure AI Cloud Platform
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.
What you build
- Integrate Projects 1–3 into one cohesive platform
- DevSecOps pipeline: image scanning, SAST/DAST, secrets scanning
- 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.
Placement & Career Support
We don't stop until you're hired.
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.
Enrol Today
Your AI & Cloud career starts here.
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.
What's included
- 16-week instructor-led program across 4 phases
- 4+ real-time, resume-ready projects + weekly labs
- Hands-on access to cloud, AI and DevOps tooling
- Course materials, recordings & doubt-clearing sessions
- 100% placement guarantee & lifetime career support
You should join if you
- Are a fresh graduate (any stream) wanting an IT career
- Want one structured path instead of scattered courses
- Learn best by building real things, not just watching
- Want serious, hands-on placement preparation
Talk to our admissions team
Call or message us for the next batch dates, fees and a personalised career roadmap. No coding experience required.
Visit / Online
Ameerpet, HyderabadClassroom & live-online batches