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AVIROwork Programs

AI DevOps Engineer

Bridge cloud infrastructure with artificial intelligence, orchestrating GPU clusters, automated continuous training pipelines, and high-throughput model serving. 10 weeks of instructor-led live training, project bootcamp, and CV marketing until you are hired.

0
hours of live training
0 weeks
at one hour a day, weekdays
0
modules
0
topics covered

Before you start

Beginner-friendly. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.

Price

India ₹69,999
United Kingdom & EU £750
Canada $1,750

GST included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.

AI DevOps Engineer Course Live Instructor-Led
AI DevOps Track Beginner to Pro
Kubernetes GPU Kubernetes GPU vLLM / Triton vLLM / Triton Ray Clusters Ray Clusters GitOps for AI GitOps for AI
Book Free Consultation
Structured Roadmap

A module-by-module breakdown

4 modules  •  24 topics covered

Everything covered in the program, in the order you'll cover it.

01 DevOps Foundations

12 hours

The delivery engineering underneath MLOps

Git branching & trunk-based development
CI/CD with GitHub Actions & GitLab
Docker assets/images & registries
Kubernetes workloads & Helm
Terraform infrastructure as code
Secrets management & least privilege

02 ML Pipelines & Automation

15 hours

Automate the model lifecycle

Training pipeline orchestration
Kubeflow Pipelines & Airflow
Data & model versioning with DVC
MLflow registry & stage promotion
Automated retraining triggers
Reproducible environments

03 Serving & Scaling

8 hours

Run inference reliably

Model servers: Triton, KServe, vLLM
GPU scheduling & node pools
Autoscaling & request batching
Canary & blue-green model releases
Cost optimisation for GPU workloads
Load testing inference endpoints

04 Observability & Governance

15 hours

Know when a model breaks

Prometheus & Grafana for ML metrics
Drift & data quality monitoring
Distributed tracing for AI services
Alerting & incident runbooks
Audit trails & model lineage
A capstone MLOps platform

The AVIROwork 4-Stage Placement Pathway

Every AVIROwork program follows a proven four-stage pathway engineered to take career switchers from zero experience through to placed UK tech professionals:

Stage 1

Live Training

50 hours of instructor-led evening and weekend classes led by senior UK industry practitioners.

Stage 2

Project Bootcamp

Build production-grade solutions, enterprise architectures, and pipelines that get peer reviewed.

Stage 3

CV Marketing

Technical CV rewrite, LinkedIn branding, and production GitHub portfolio polish.

Stage 4

Placement Support

Direct candidate introduction to employer networks and technical interview drills until hired.

Hands-On Portfolio

Five projects, in AI DevOps Engineer

The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.

01

01 · Foundations

A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.

02

02 · Multiple moving parts

A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.

03

03 · Architecture that scales

A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.

04

04 · An enterprise scenario

A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.

05

05 · Capstone

End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.

What's included

Everything in the programme

The same seven things in every programme on the site — the subject is what changes.

Live training

Taught live by a practitioner, not pre-recorded video you work through alone.

50 hours

Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.

Projects

Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.

Mentoring

One mentor who stays with you for the whole programme, rather than whoever is free that week.

Resume & LinkedIn

Both rewritten around the work you actually built, and positioned for the roles you are targeting.

Interview prep

Mock interviews on the technical and the competency side, including defending the projects you built.

Placement support

Your profile put in front of employers, and the search continues until you are hired.

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