You Know CI/CD. Now Make It Work for AI.
From DevOps Engineer → MLOps / LLMOps Engineer
You already understand pipelines, automation, containerisation, observability, and reliability engineering. The AI stack is not a parallel universe — it is your world, extended. This programme closes the remaining 20% precisely and efficiently.
Eight skill areas that turn your DevOps expertise into AI infrastructure leadership.
Designed specifically for DevOps and Platform Engineers.
Takes less than 60 seconds. We get back to you within a day.
Every module ends with a real deliverable built on an ops scenario. Not theory — something you can deploy and demo.
The demand for DevOps engineers who understand AI infrastructure is one of the most documented shortages in tech right now.
You already know pipelines, automation, observability, and reliability. The AI stack is not a parallel universe — it is your world, extended. This closes the remaining gap.
The tools being listed in MLOps and LLMOps job descriptions right now.
All leverage your existing DevOps / SRE experience — now applied to AI systems.
Build and manage ML pipelines, model registries, and deployment infrastructure. Active shortage in every AI-building company right now.
Deploy and monitor LLM-powered services in production. The newest and fastest-growing role in AI infrastructure.
Build the internal platforms that AI teams use to train, evaluate, and ship models. High autonomy, high impact, high compensation.
Your seniority is exactly why this works. You already understand production systems at depth — the part most AI engineers lack entirely. This programme gives you the AI-specific vocabulary and toolset to apply your existing instincts to AI infrastructure.
Absolutely. The moment your company ships any AI service, someone needs to instrument it, monitor it, and manage its deployment lifecycle. After this programme, that person is you.
Yes. Cloud infrastructure, IaC, and API management are core to MLOps. The programme suits Cloud Engineers, SREs, Platform Engineers, and anyone who works on deployment and observability.
At least 1-2 years of hands-on DevOps, SRE, or cloud infra experience is recommended. The programme assumes you understand CI/CD, containers, and API concepts.
Weekends only — Saturday and Sunday, 4 hours each day. About 8 hours a week. No weekday commitments, no leave required.
Both. Attend in-person at our Bangalore centre, or join live online from anywhere. All sessions are recorded and available within 24 hours.
Sessions are recorded and accessible within 24 hours. You also get written notes, the project brief, and access to mentor office hours so you never fall behind.
Some familiarity helps but is not required. Module 1 covers Python in the context of DevOps tooling. If you have scripted in Bash or any language, you will pick it up quickly.
The demand for MLOps and LLMOps engineers is a documented shortage right now. Every participant who completes gets 100% placement support — resume prep, portfolio review, 1:1 mock interviews, and referrals to our hiring network.
Rs.25L–Rs.60L depending on experience and company. Companies building serious AI products pay a significant premium for people who can bridge infrastructure and AI.
Many participants do. As soon as your organisation builds anything with AI, the gap in deployment and observability becomes obvious. You become the person who solves it — and gets recognised for it.
Yes. Flexible EMI plans starting from Rs.0 down payment — zero-cost, meaning no added interest. Our team walks you through the options when you apply.
Yes. Bluetick AI Academy completion certificate plus a portfolio URL — real deployed projects, which carries far more weight in MLOps hiring than any certificate PDF.
Next batch fills fast.
Apply Now → Secure Your Seat