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.

Enrol Now

Our Partners

Logos of NVIDIA, Google Cloud Platform, AWS, Databricks, and Microsoft Azure
Woman standing and speaking to a group of people seated at a conference table in a bright room with large windows and white walls.

Deploy autoscaling KServe/KEDA endpoints in the cloud—monitor cost & drift

Woman wearing a virtual reality headset in a tech store, surrounded by floating digital interface and glowing sparks.

Design safe, compliant LLM systems—bias, privacy & PII guard-rails built-in

Group of five young professionals working together around a computer in an office with a chalkboard wall.

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.

Smiling man with short hair and beard wearing a black Nike t-shirt, standing against a white background.

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.

Advanced: Generative AI Systems & Safety Advanced: Generative AI Systems & Safety
Quick View
Advanced: Generative AI Systems & Safety
$4,250.00

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.

Student Outcomes

  • "I recently completed a course at AI Tech Institute and was thoroughly impressed. The curriculum was thoughtfully structured, the instructor was truly dedicated, and the overall learning experience delivered exceptional value."

    Maria

  • "Taking the Machine Learning course at AI Tech Institute was a game changer. It gave me the skills and confidence to break into AI and helped me land a job soon after. The content was practical and beginner friendly, with clear lessons and hands-on projects that made a real difference during interviews."

    Pani

  • "Very valuable course. Amir is a great instructor, explained how machine learning used in the real world in a easy way. Highly recommend for people who want to know more about AI or work in this area."

    Hoàng Phước

  • "The AI and Data science bootcamp was one of the most impactful learning experiences I’ve had. The curriculum was well structured. The instructor was knowledgeable and supportive, and the course provided a strong balance between theory and hands on labs. Highly recommended!"

    Ehsan

  • "I took a course at AI Tech Institute, and it genuinely exceeded my expectations. The content was not only clear and well-structured, but also incredibly relevant to the challenges I deal with day-to-day at work."

    Rosha

  • "Extremely happy with clear, well designed and applicable syllabus to be implemented in day-to-day need of nowadays coding ability. Coming from non-IT background, it is a valuable experience of seamlessly and effortless the teaching was, for me to understand the complex subjects. Would definitely recommend to colleagues and friends!"

    Kresna

  • "Absolutely fantastic experience with AI Tech Institute! I couldn’t be happier with the quality of education and support I received here. The instructors are true experts — knowledgeable, approachable, and always willing to help students succeed. The curriculum is up-to-date with the latest advancements in AI and tech, blending theory with plenty of hands-on projects that really build practical skills."

    Foad

  • "Amir is both technically proficient, and a very good teacher. He was attentive to where each person in the group was and paced the information flow accordingly. I also found him very approachable, encouraging and supportive."

    Francois

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.