How long is the LLMOps Bootcamp?

12 weeks at about 15 hours a week. The weeks run as six two-week blocks; each block ends in a project you deploy and a gate you must pass before the next block opens.

Is the LLMOps Bootcamp live or self-paced?

Live. It is taught on Zoom by one instructor, with a 30-minute one-to-one every week. Nova, Square 1's AI tutor, grades every exercise and project between sessions against a rubric you can read.

When are the live sessions for the LLMOps Bootcamp?

Live sessions run on weekday evenings from 5:30 PM Sri Lanka and India time, with full-day sessions at weekends. That is 5:00 PM in Pakistan, 5:45 PM in Nepal and 6:00 PM in Bangladesh, so the sessions fit around a working day.

How much does the LLMOps Bootcamp cost?

A$200 for the founding cohort, paid once; the standard price is A$400. It is the same price in every country. Founding students keep their founding price on any second programme.

What will I build in the LLMOps Bootcamp?

6 deployed projects: Serving, Gateway, Observability, Release pipeline, The platform and Employer brief. Each is graded against a published rubric and kept on a record an employer can open at /verify.

What skills does the LLMOps Bootcamp teach?

Python, Kubernetes, vLLM, gateways, tracing, CI/CD and SLOs. It prepares you for roles such as LLMOps engineer, ML platform engineer, AI infrastructure engineer and SRE (AI).

Does the LLMOps Bootcamp guarantee a job?

No. No placement rate is published because the first cohorts have not graduated. What you leave with is six deployed projects, a recorded defence of your code and a hiring plan worked through with your instructor, including a two-week hiring sprint at the end.

Live bootcampTaught live on Zoom, one instructor

LLMOps Bootcamp

Name: LLMOps Bootcamp
Price: 200.00 AUD
Availability: PreOrder

Serve, observe and pay for LLMs in production without surprises.

LLMOps Bootcamp is a 12-week live online bootcamp, taught on Zoom by one instructor at about 15 hours a week, with 6 deployed projects and a one-to-one every week. The founding price is A$200, down from A$400. Live sessions run on weekday evenings from 5:30 PM Sri Lanka and India time, with full-day sessions at weekends.

Taught with

PythonDockerKubernetesAWSTerraform

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By week 12, you will have shipped

Week 1Local Model ServerA Docker container serving a small open model with a health check endpoint
Weeks 1–2ServingA self-hosted model endpoint with load tests, deployed
Week 3Retry WrapperA Python client that retries a model call with backoff and logs every failure
Weeks 3–4GatewaysA gateway in front of three models with fallback, deployed
Weeks 5–6ObservabilityA tracing and cost dashboard for a real application, deployed
Week 7Prompt Version StoreA versioned prompt registry in Git with a script that diffs two releases
Weeks 7–8Release engineeringA release pipeline with canary and rollback for prompts and models
Week 9Autoscaling PolicyA Kubernetes autoscaling policy for the model endpoint, tested under a load script
Weeks 9–10Reliability at scaleThe capstone: a production LLM platform with SLOs, runbooks and a cost model
Weeks 11–12Employer brief and hiring sprintThe employer brief shipped, and demo day

weeks, live on Zoom: 12
hours a week: 15
projects to build: 10
minutes 1-1, every week: 30

Who it is for, and what you receive.

Backend and DevOps engineers who are being asked to run LLM systems and want to do it without surprises.

Before you start

Backend or DevOps experience; Docker and one cloud.

Skills

PythonKubernetesvLLMgatewaystracingCI/CDSLOs

A week, about 15 hours

Live on Zoom: 4h
Set reading and exercises: 6h
The project: 5h

1
Six deployed projects
Each graded by Nova against a published rubric and signed off by the instructor at the gate.
2
The twelve-week plan
Every week has its theme, its topics and what you build, published before you apply and taught live by your instructor.
3
Live on Zoom every week
A 90-minute live code review, a 60-minute squad lab, 60 minutes of office hours, and a 30-minute 1-1 with your instructor.
4
Your hiring plan
Opened in week 1 and reviewed in every 1-1: target roles, the gap map, the proof list, weekly actions, an interview log, the outcome.
5
The record
Every grade and every project with its repository at square1ai.com/u/{handle}, and the recording of your viva, verifiable by any employer.
6
The certificate
A credential ID that resolves at square1ai.com/verify to the real completion.
7
The hiring sprint
Weeks 11 and 12: CV and portfolio from graded work, applications, mock interviews scored against the role, demo day.
8
Nova for twelve weeks
A tutor with every submission of yours in its memory, at 2am as well as in class.

Twelve weeks in six parts.

Each part teaches for a week, then you build and deploy a project, then a gate checks it before the next part opens. The project and its gate are the part's entry on your record.

Live time each week on Zoom: a 90-minute class where the instructor reviews real submissions, a 60-minute squad lab, 60 minutes of office hours, and your own 30-minute 1-1. Nothing is lectured live; the recorded lessons do that.

Key skillsHosted APIs versus self-hostingServing open weights with vLLMBatching, streaming, quantisation
Week 1
Serving
- Hosted APIs versus self-hosting
- vLLM
- Batching, streaming, quantisation
- GPUs and cost
You build: A model endpoint
Week 2
Project 1: serving
- Load testing
- p95 reporting
You build: Self-hosted endpoint with load tests
Project 1 · the gate at week 2
Serving
A self-hosted model endpoint with load tests, deployed
You hand in
- vLLM deployment
- Load tests
- p95 report
The gate
Throughput and p95 latency under load reported.

What you can do by week 12.

The platform layer every company running LLMs needs: serving, a gateway, observability, safe releases and runbooks, built once and carried to any team.

1
Serve open-weight models with vLLM, batching, streaming and quantisation, and load-test them.
2
Build an LLM gateway with routing, retries, fallbacks, rate limits, budgets and caching.
3
Trace prompts and tool calls, account for tokens and cost, and surface quality signals online.
4
Version prompts and models, run canary releases and roll back automatically.
5
Plan capacity, respond to incidents and manage secrets for AI systems.
6
Defend a platform's design, cost and incident history.

Roles this prepares you for

LLMOps engineerML platform engineerAI infrastructure engineerSRE (AI)

No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.

Your record at week 12.

Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.

Graded, line by line: Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
Six gates: A block does not open until the previous project passes its gate. You always know where you are and what is next.
A weekly 1-1: Thirty minutes with your instructor, who has already read your code before the call.
One page an employer can run: Every grade, project and the viva recording at /verify. An employer opens it, runs the code and watches you defend it.

Record, LLMOps BootcampExample

Week 2
Serving
Throughput and p95 latency reported under load
Graded, gate signed
Week 4
Gateway
A provider outage is survived without user-visible errors
Graded, gate signed
Week 6
Observability
Every request is attributable to a feature and a cost
Graded, gate signed
Week 8
Release pipeline
A bad release is rolled back automatically
Graded, gate signed

The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.

8 entries, twelve weeks. Reserving a spot holds the founding price.

Reserve a seat

How we help you find a job.

The last block is not curriculum. It is the hiring sprint, and the proof you built in the ten weeks before it.

The course booklet

The whole plan on a few pages: the parts, the projects, the gates and the price. We email it to you.

Get the booklet

1
Your hiring plan, from week 1
Six parts you and your instructor keep: target roles, the gap map from real postings, the proof to send, weekly actions, an interview log, the outcome. Read before every 1-1.
2
The hiring sprint
Weeks 11 and 12: CV, portfolio, applications, mock interviews, and demo day in front of hiring partners.
3
A record an employer can run
Your deployed projects and the recorded viva on /verify. An employer opens it, runs the code and watches you defend it.
4
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
5
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.

Questions people ask.

About LLMOps Bootcamp, answered from the plan on this page.

One cohort, one instructor. Hold your spot.

A cohort of 25, one instructor, 12 weeks, six things you ship. Spot holders hear the start date first, and nothing is charged before you confirm.

Six deployed pieces of LLM platform infrastructure
A cost model and runbooks a team can adopt
A recorded viva
Your record on /verify

A$200~~A$400~~