Are you ready to meet the demands of tomorrow? Evaluate your approach to skilling.
Build & Govern Generative-AI Products That Enterprises Trust
12 Weeks. Live Online Classes. Instructor-led.
Our Partners
Deploy autoscaling KServe/KEDA endpoints in the cloud—monitor cost & drift
Design safe, compliant LLM systems—bias, privacy & PII guard-rails built-in
Build Retrieval-Augmented Generation (RAG) services with vector databases
What you will learn?
-
Fine-tune and evaluate transformers on Hugging Face
Set up experiment tracking, environment repro with Conda/Poetry
-
Build vector indexes with pgvector or Pinecone
Chain prompts with LangChain; hit <300 ms median latency
-
Measure factuality, toxicity, bias; create automated regression suites
Integrate guard-rail frameworks (Guardrails.ai / Rebuff)
-
Containerise with Docker multi-stage; deploy via KServe & Helm
Autoscale with KEDA; emit Prometheus metrics, Grafana dashboards
-
Add JWT/OAuth, rate limiting, and data-loss-prevention filters
Generate audit logs for SOC-2 / ISO 27001 compliance
-
Team project on real enterprise data (finance, energy, retail)
Deliver a live URL, design doc, and ROI/cost analysis
Unlock the full power of large-language models while meeting enterprise-grade security and compliance. In this 12-week cohort you’ll design and deploy Retrieval-Augmented Generation (RAG) services on Kubernetes, wire in bias- and PII-guard-rails, and build cost dashboards that keep CFOs happy. You’ll graduate with a live KServe endpoint, an MLflow-tracked fine-tune, and a governance playbook—exactly what hiring managers expect from a modern Generative-AI or LLM Engineer.
Who Should Enrol?
Engineers and data professionals who need to ship Gen-AI features to production without compromising safety or cost.
Prerequisites
Solid Python & Git, basic ML/LLM awareness, plus ~8-10 hrs/week for classes & labs.
(Free Deep Learning Core & Docker-K8s Mini-camp bridge badges are provided for all Intermediate-track graduates.)
-
You’re already training models; now you need to deploy Gen-AI services that meet latency, cost and safety SLAs.
-
You want to embed LLM calls, vector search or RAG pipelines into existing apps without turning production into a research lab.
-
Move beyond notebooks to shipping generative models behind robust APIs—with monitoring, A/B guards and rollback plans as well as RAG.
-
Become the go-to operator for GPU workloads, prompt-traffic observability and secure model–data pipelines.
-
Fast-track into one of tech’s hottest niches by learning the end-to-end Gen-AI stack, from fine-tuning to feature flags.
Career Pathways
-
Design, tune and deploy LLM workflows that power chatbots, copilot features and creative-content engines.
-
Build retrieval-augmented generation pipelines that fuse proprietary data with frontier models—safely and at scale.
-
Package and orchestrate models with CI/CD, feature stores, vector DBs and on-call monitoring dashboards.
-
Translate fuzzy product briefs into user-centric Gen-AI features, balancing UX, cost and compliance.
-
Scope end-to-end Gen-AI systems for enterprise clients, from data governance to cloud spend optimisation.
Graduates leave with a live Gen-AI service, a public GitHub repo, a design document, and recruiter-friendly talking points—aligned to >70 % of current “LLM Engineer” job posts.
Amir Charkhi
Technology leader | Adjunct Professor | Founder
With 20 + years across energy, mining, finance, and government, Amir turns real-world data problems into production AI. He specialises in MLOps, cloud data engineering, and Python, and now shares that know-how as founder of AI Tech Institute and adjunct professor at UWA, where he designs hands-on courses in machine learning and LLMs.
Advanced: Generative AI Systems & Safety
12 Weeks. Live Online Classes. Next Cohort 2nd September
-
![]()
“In just six weeks I went from zero Python to building a churn-prediction model I could demo at work. I finally feel job-ready.” — Maddie, Data Analyst
-
![]()
“The pace was perfect—hands-on from day one and always tied back to real business value. Best learning experience I’ve had online.” — Demian, Mining Engineer
-
![]()
“Being a new mum, flexible recordings were a lifesaver. I could pause, practice, and still keep up with the cohort.” — Molly, Graduate Analyst
-
![]()
“Clear path: beginner foundations, then straight into their Intermediate Data Science track. No other provider lays out the journey so cleanly.” — Bec, Business Analyst
-
![]()
“Projects mirrored the stack we use at work—no toy examples. I deployed my capstone on AWS the same night.” — Amy, Cloud Architect
-
![]()
“From pandas to production MLOps in 24 weeks—and I never felt lost. The Slack support channel replies within minutes.” — Arjun, Energy Sector Intern
Frequently Asked Questions
-
Beginner courses: none— we start with Python basics.
Intermediate & Advanced: ability to write simple Python scripts and use Git is expected. -
Plan on 8–10 hours: 2× 3-hour live sessions and 2–4 hours of project work. Advanced tracks may require up to 10 hours for capstone milestones.
-
All sessions are recorded and posted within 12 hours. You’ll still have access to Slack/Discord to ask instructors questions.
-
New intakes launch roughly every 8 weeks. Each course page shows the exact start date and the “Apply-by” deadline.
-
Just a laptop with Chrome/Firefox and a stable internet connection. All coding happens in cloud JupyterLab or VS Code Dev Containers—no local installs.
-
Yes. 100 % refund until the end of Week 2—no questions asked. After that, pro-rata refunds apply if you need to withdraw for documented reasons.
-
Absolutely. We issue invoices to companies and offer interest-free 3- or 6-month payment plans.
-
Live Q&A in every session, 24-hour Slack response time from instructors, weekly office-hours, and code reviews on your GitHub pull requests.