AI Cloud Engineer Bootcamp (DevOps, Kubernetes, Terraform & RAG)
6 weekly Monday-evening sessions on AWS, Kubernetes, Terraform, DevOps/SRE, and AI/RAG/AgentOps — finishing with an end-to-end agentic AI capstone (db-agent).
CAD $299 launch price. Next cohort: June 15, 2026 · Mondays, 6–8 PM EST.
Program Details & Pricing
Schedule
- Duration: 6 weeks live (2 hrs/week · ~12 hrs total) + ongoing Slack mentoring
- Format: Live cohort · Online (Zoom + Slack)
- Schedule: Mondays, 6–8 PM EST
- Next cohort: June 15, 2026
What's Included
- ~12 hours of live instruction (6 Monday evenings, 6–8 PM EST)
- Hands-on AWS, Kubernetes, Terraform, DevOps/SRE & AI infrastructure labs
- End-to-end agentic AI data engineering capstone (db-agent) deployed on your own EKS cluster
- 1:1 capstone feedback session
- Ongoing Slack lab support throughout all 6 weeks — questions answered between sessions
- 1-hour interview preparation session after Week 6 — resume, LinkedIn, technical interview patterns
- Lifetime access to the beCloudReady Slack community
- Eligible to re-attend future cohorts at no extra cost
- Portfolio & resume review
- Completion certificate
- Job-prep resources & hiring partner referrals
About this program
DevOps fundamentals plus the AI infrastructure layer the role is evolving into — built around a real agentic AI capstone, not toy tutorials.
- Foundation: AWS, Kubernetes, Terraform, CI/CD, SRE — production-grade
- AI layer: dedicated week on RAG and AgentOps — vector DBs, LLM endpoints, agent observability, cost guardrails
- Capstone: end-to-end agentic AI data engineering project (db-agent) deployed on your own EKS cluster
- 6 Monday evenings · 2 hrs each · ongoing Slack lab support between sessions
- 1-hour interview prep after Week 6, lifetime Slack community access, free re-attendance of future cohorts
Free Live Webinar
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What you'll achieve
- Deploy and manage production workloads on AWS (EC2, EKS, RDS, S3, IAM)
- Architect Kubernetes clusters from scratch and manage them with Helm & ArgoCD
- Automate all infrastructure with Terraform modules, workspaces, and state backends
- Build end-to-end CI/CD pipelines with GitHub Actions including blue-green deployments
- Set up observability stacks with Datadog or Grafana, SLOs, and incident runbooks
- Write Infrastructure as Code that passes security and compliance checks
- Ship a small RAG pipeline (vector DB + embedding service + retrieval API + LLM endpoint) onto your EKS cluster
- Build a portfolio of 3+ deployed, production-grade projects including AI infrastructure
- Land a Cloud Engineer, DevOps Engineer, SRE, or AI Cloud Engineer role
What students say
“I recently started working as Site Reliability Engineer for Oxford Properties. Special thanks to Chandan and the BCR team. The concepts and implementations I learnt at BCR and the projects I worked on gave me confidence and a broader perspective about DevOps and Cloud technologies. The videos strengthened my fundamentals and helped in my technical interview rounds. Just be patient and believe in yourself & the process.”
Annaya Mohanty
Bootcamp Graduate → Site Reliability Engineer, Oxford Properties
“I underwent training on the DevOps course at beCloudReady. They provide excellent training sessions on Git, Python, Ansible, Terraform, Jenkins, and Networking concepts, with Q&A sessions every day to resolve troubleshooting issues. I definitely recommend everyone who wants to learn DevOps.”
Kunal Killekar
Bootcamp Graduate → Platform Engineer
“During my time at BCR I learned way more than I could've ever thought. You'll be applying a lot of the knowledge you gain from the DevOps program. The experience requires you to think critically and analytically. This experience is worth more than any projects you do on your own and gives you the confidence when applying for new opportunities.”
Aaron Hong Phu
Career Changer → DevOps Engineer
Curriculum
Week 1 — Cloud Foundations
- VPC, IAM, EC2, S3, RDS deep dive
- Networking, Security Groups & NACLs
- Cost optimisation & billing best practices
- AWS CLI & SDK fundamentals
- Lab: stand up a 3-tier VPC with a bastion + least-privilege IAM policy
Week 2 — Containers
- Docker deep dive — images, layers, networking, volumes, multi-stage builds
- Kubernetes architecture (pods, deployments, services, ingress)
- EKS cluster setup on AWS
- Helm charts & GitOps with Argo CD
- Lab: containerise a FastAPI app, push to ECR, deploy to EKS via Helm + Argo CD
Week 3 — Terraform
- Terraform modules & workspaces
- Remote state with S3 + DynamoDB locking
- Drift detection & remediation
- Security scanning with tfsec
- Lab: rewrite Week 1's VPC + EC2 as Terraform modules, dev/prod workspaces, demo drift
Week 4 — DevOps / SRE
- GitHub Actions — build, test, deploy workflows; blue-green & canary
- Container registry workflows + image scanning
- Secrets management (AWS Secrets Manager / Vault)
- SLOs, error budgets, alerting with Grafana / Datadog
- Log aggregation & distributed tracing with OpenTelemetry
- Incident response playbooks & runbooks
- Lab: GitHub Actions → ECR → Argo CD blue-green deploy + Prometheus/Grafana + one SLO + incident runbook
Week 5 — AI, RAG, and AgentOps
- Why every production AI agent is a distributed system, not a notebook
- LLM serving — hosted endpoints vs. self-hosted (vLLM, TGI) on GPU nodes
- Vector databases — pgvector, Pinecone; embedding workers; index lifecycle
- RAG pipeline architecture — retrieval API, reranking, evals
- AgentOps — agent orchestration, tool registries, LLM observability, cost guardrails, prompt versioning
- SQL-safety patterns for agents that touch real data (read-only roles, query budgets, schema allow-lists)
- Lab: layer a small RAG service on the EKS cluster — pgvector + embedding worker + retrieval API + LLM endpoint
Week 6 — Capstone Project: End-to-End Agentic AI Data Engineering (db-agent)
- Ship one working system end-to-end: an agentic text-to-SQL pipeline, deployed on the cloud you've been building all along
- Backend: FastAPI retrieval + LLM endpoint (Week 5) on the EKS cluster (Week 2), provisioned by Terraform (Week 3), deployed via GitHub Actions + Argo CD (Week 4)
- Read-only SQL execution path with query budgets and schema allow-lists — the safety pattern that keeps agents out of production data
- Observability — traces, token-cost dashboards, SLO on retrieval latency
- Deliverables: working demo, architecture diagram, runbook, short demo video — the portfolio piece you point recruiters at
After Week 6 — 1-Hour Interview Prep Session
- Resume preparation for Cloud, DevOps, SRE, and AI Cloud Engineer roles
- LinkedIn profile optimisation — headline, about, featured work, recruiter signals
- GitHub profile as your portfolio — pinned repos, README, contribution graph, capstone showcase
- Technical interview question patterns + job hunting strategy — where to apply, recruiter outreach, mock interview tips
How does this compare?
See how the Full Stack AI Bootcamp stacks up against the alternatives.
| Feature | beCloudReady Bootcamp | Traditional Bootcamp | Udemy Course |
|---|---|---|---|
| Duration | 6 weeks live (2 hrs/week · ~12 hrs total) + ongoing Slack mentoring | 12–24 weeks | Self-paced (often abandoned) |
| Price | CAD $299 | $5,000–$20,000 | $10–$30 |
| Live instruction | ✓ | ✓ | ✗ |
| AI tools workflow (Cursor, Claude) | ✓ | Rarely | ✗ |
| Real production app | ✓ | ✓ | ✗ |
| 1:1 feedback | ✓ | Limited | ✗ |
| Job-ready focus | ✓ | Varies | ✗ |
| Certificate | ✓ | ✓ | ✓ |
| Community (Slack) | Lifetime | Limited | ✗ |
Who should join
- Backend or software engineers pivoting to cloud and infrastructure
- IT professionals seeking cloud-native and DevOps skills
- Bootcamp graduates targeting higher-paying engineering roles
- Anyone who has cloud certifications but lacks hands-on production experience
Prerequisites
- Basic Linux command line familiarity
- Comfort with at least one scripting language (Bash or Python)
Frequently asked questions
Do I need cloud experience before joining?
No prior cloud experience is required. You need basic Linux command line familiarity and comfort with at least one scripting language (Bash or Python). If you can SSH into a server and write a simple script, you're ready.
Is the CAD $299 price only for the first cohort?
Yes. CAD $299 is our introductory launch price, limited to the first 25 students. The regular price will be CAD $1,499 after the launch cohort closes. Enroll now to lock in the early-bird rate.
How much time does this require?
6 weekly Monday-evening sessions of 2 hours each (~12 hours of live instruction total), running 6–8 PM EST. Plan on 1–3 hours of lab work between sessions to finish the week's exercise. The program used to be a 5-day Mon–Fri intensive — recent cohorts found that pace overwhelming, so we now spread the same content across 6 weeks so you can actually retain it. Slack lab support runs throughout all 6 weeks, and there's a 1-hour interview prep session after Week 6.
What if I miss a live session?
All live sessions are recorded and available within 24 hours via the Slack workspace. Instructors also respond to async questions in Slack so you can catch up without falling behind.
Will I get a certificate?
Yes. You'll receive a beCloudReady completion certificate after finishing all 6 weeks and submitting your capstone project. The certificate details the specific AWS, Kubernetes, Terraform, CI/CD, and AI infrastructure skills you've demonstrated.
Does this prepare me for AWS certifications?
The bootcamp content closely maps to the AWS Solutions Architect Associate and AWS DevOps Engineer Professional exams. Many graduates sit these certifications after completing the 6 weeks. However, our primary goal is hands-on job-readiness for DevOps and AI Cloud Engineer roles, not exam prep.
Why is this called "AI Cloud Engineer" — is it still a DevOps bootcamp?
AI Cloud Engineer is the name the hiring market is moving toward for the evolved DevOps role. The foundation is unchanged: AWS, Kubernetes, Terraform, Docker, CI/CD, SRE — all the DevOps essentials. The difference is Week 5 is dedicated to AI, RAG, and AgentOps, and Week 6 is an end-to-end agentic AI data engineering capstone (db-agent) — so you graduate with real AI infrastructure on your portfolio. Job titles you'll be ready for: DevOps Engineer, SRE, Cloud Engineer, Platform Engineer, and AI Cloud Engineer / AI Platform Engineer.
Do I need any AI / machine learning background?
No. The RAG capstone is taught from first principles — we explain what a vector database is, how embeddings work, and how to wire a retrieval API in front of an LLM endpoint. If you can run a Docker container and write a basic Python function, you're set. You don't need a math or ML background.
Is there job placement support?
Yes. All graduates receive resume and LinkedIn review, mock technical interviews, and access to exclusive job postings from our startup and scale-up hiring partners via the Slack community. You also get lifetime Slack access and are eligible to re-attend future cohorts at no extra cost — useful when a new module is added to the curriculum.
Is there a money-back guarantee?
Yes. If you attend the first week and feel the bootcamp isn't right for you, we'll issue a full refund within 7 days of the start date — no questions asked.
Ready to get job-ready?
Join the first cohort at our introductory price of CAD $299. Limited to 25 students.
Related Programs
Regular: CAD $1,499
Next cohort: June 15, 2026
Mondays, 6–8 PM EST
Apply or enquire
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