Generative & Agentic AI Career Program
Build and deploy enterprise AI on Google Cloud: RAG, multi-agent systems, tool calling and agents that run in production.
About this program
Why this career pays off
Every company is trying to put Generative AI to work, and the shortage is not people who can chat with a model — it is people who can build an AI system that survives production. This program takes you there in seven modules on Google Cloud. You start with your own GCP account and Claude as your development assistant, then build a retrieval-augmented course assistant on Vertex AI, a team of agents that assess a student and act on what they find, tool calling that triggers real cloud functions, PII masking and private model endpoints for sensitive data, and finally a containerised agent deployed on Kubernetes with autoscaling. Every module is one working project you build yourself, so you finish with deployable work and a portfolio, not slides.
What you'll learn, module by module
A complete, no-surprises view of the curriculum. Click any module to expand.
01 Cloud Fundamentals
- Project: your own Google Cloud account, set up properly
- Stack: GCP Cloud Console, IAM, Billing
- Create a Google Cloud Platform account
- Set up a billing account
- Navigate the GCP Cloud Console
- Identity and Access Management (IAM)
- Create a new GCP project
- Enable the APIs a project needs
- Budget alerts, so a lab never surprises you
02 Claude AI Basics
- Project: Claude as your AI development assistant
- Stack: Claude, Anthropic Console
- Create an Anthropic account and access Claude
- What Claude can and cannot do; the interface
- Prompting for code generation
- Brainstorming the architecture of a real use case
- Iterating with Claude to debug and refine an implementation
- Documenting your work and generating explanations
- Using Claude through the rest of the program
03 Foundational RAG and Vector Databases
- Project: an AI-powered course assistant
- Stack: Vertex AI, Vertex AI Search, Cloud Storage, Document AI
- Ingest course PDFs into Cloud Storage
- Extract text with Document AI
- Generate embeddings with Vertex AI Text Embeddings
- Store embeddings in Vertex AI Vector Search
- Build a query interface in Python and FastAPI
- Retrieve the chunks that match a student's question
- Generate the answer with Vertex AI Gemini
04 Multi-Agent Orchestration
- Project: autonomous student mentorship
- Stack: LangGraph / CrewAI, Vertex AI, Cloud Functions, Cloud SQL
- Define the agent roles: assessor, recommender, tutor
- Set up Vertex AI Gemini for agent reasoning
- Connect the agents to student data in Cloud SQL
- Inter-agent communication with LangGraph
- Agent 1 analyses quiz scores
- Agent 2 fetches personalised resources from Cloud Storage
- Agent 3 drafts the mentorship email to the student
05 API and Tool Calling
- Project: dynamic course enrollment and scheduling
- Stack: Cloud Functions, Cloud Workflows, Vertex AI function calling
- Cloud Functions for the enrollment logic
- Cloud Workflows for email notifications
- OpenAPI specifications for your tools
- Gemini function calling inside the application
- Extract course name and date from what the user types
- The model triggers the function that enrolls the student
- Gemini triggers the workflow that sends the confirmation
06 Security and Privacy
- Project: PII masking for student records
- Stack: Cloud DLP, Secret Manager, Vertex AI private endpoints
- Store raw student feedback in Cloud Storage
- Identify PII — names, e-mail addresses — with Cloud DLP
- Mask PII before anything is processed
- Keep API keys in Secret Manager
- Deploy a private model endpoint from Vertex AI Model Garden
- Sentiment analysis on masked feedback with the private model
- Store anonymised insights in a secure database
07 Scalable Cloud Deployment
- Project: a code grader agent in production
- Stack: GKE, Artifact Registry, Docker, FastAPI
- Build the code grader agent in Python and FastAPI
- Write the Dockerfile for the application
- Build and push the image to Artifact Registry
- Provision a GKE (Kubernetes) cluster
- Kubernetes deployment and service YAMLs
- Deploy the agent and configure load balancing
- Autoscaling for submission spikes
Who this program is for
- Graduates (IT or non-IT) who want to start their career in Generative AI
- Developers and support engineers moving into AI engineering
- Cloud, DevOps and data professionals adding AI agents to their profile
- Anyone who has used ChatGPT or Claude and now wants to build with them properly
Roles you can target after
- AI Engineer (Generative AI)
- Agentic AI Developer
- LLM Application Developer
- AI Solutions Engineer (Google Cloud)
- AI Platform / MLOps Engineer
Tools & technologies covered
Google Cloud Platform Vertex AI Gemini Claude (Anthropic Console) Vertex AI Vector Search Document AI Cloud Storage Cloud Functions Cloud Workflows Cloud SQL Cloud DLP Secret Manager Vertex AI Model Garden GKE (Kubernetes) Artifact Registry Docker Python FastAPI LangGraph CrewAI IAM
Common questions
SHA Cloud Career Path ?
Foundation ->Cloud Administration ->Cloud Support / Junior Cloud Engineer ->Cloud Engineer ->Senior Cloud Engineer->Cloud / Solutions Architect
Database Architect path?
Database Foundation:→ Junior DBA / Database Support→ DBA→ Senior DBA / Database Engineer→ Database Architect
Can I directly work as Database Architect?
No, not at all. Below is the path you have to follow:
Database Foundation:→ Junior DBA / Database Support→ DBA→ Senior DBA / Database Engineer→ Database Architect
Can I joined directly as Cloud Architect?
No, Not at all.
Foundation ->Cloud Administration ->Cloud Support / Junior Cloud Engineer ->Cloud Engineer ->Senior Cloud Engineer->Cloud / Solutions Architect
Don't Know Which IT Career Is Right for You?
You don't need to choose a technology before understanding the career. Your IT Career Map:
Graduate->Understand your background & strengths->Choose a direction->If you enjoy...Infrastructure, systems, servers, then a possible path is Cloud / Infrastructure-> If you enjoy...Databases, data, backend systems, then a possible path is Database / Cloud DBA. If you enjoy...Programming + mathematics + analytics, then a possible path is Data / AI. If you enjoy...Automation + infrastructure, then a possible path is DevOps / Cloud Engineering. If you enjoy...Security & systems, then a possible path is Cloud Security / Cybersecurity. If you enjoy...Multiple cloud platforms, then a possible path is Multi-Cloud / Gen & Agentic AI and on ......Final steps once you decide: Foundation → Specialisation → Projects → Interview Preparation → Job Search _> Got placement. So you need one mentor for this guidance who himself works in the IT Industry that to minimum of 10- 12 years.