Master the Infrastructure Behind the AI Revolution

As foundation models transform every industry, a new engineering discipline has emerged: LLMOps—the specialized practices for deploying, scaling, and maintaining large language models and agentic AI systems in production. This cutting-edge 8-week program makes you one of the few engineers qualified to build the infrastructure powering the future of AI.

The LLMOps Engineering Advantage

While traditional software engineers struggle to adapt to the unique challenges of operationalizing foundation models, you'll gain specialized expertise in:

Vector Databases: Design and scale retrieval-augmented generation systems

AI Agent Infrastructure: Build platforms for deploying and monitoring agentic AI

Multi-modal AI Systems: Architect infrastructure combining vision, language, and voice

Specialized Evaluation: Monitor, debug, and improve LLM performance in production

Your Career After This Course:

LLMOps Engineer ($165,000 - $210,000)

AI Infrastructure Architect ($180,000 - $225,000)

RAG Systems Engineer ($170,000 - $215,000)

AI Agent Platform Engineer ($185,000 - $230,000)

Salary ranges based on May 2025 data for mid-level positions at companies with significant AI initiatives

What Sets This Program Apart

LLM-Specific Focus: Unlike generic MLOps courses, we focus exclusively on operationalizing foundation models
Agentic Systems: Learn to build infrastructure for the next generation of autonomous AI agents
RAG Infrastructure: Master the specialized engineering behind enterprise knowledge systems
Production Challenges: Tackle the unique scaling and monitoring challenges of foundation models

What Past Students Are Saying

Elena Kowalski, VP of AI Infrastructure, Enterprise SaaS Company

"We interviewed dozens of candidates for our LLMOps team, but less than 5% had the specialized knowledge needed. Graduates of this course understand the unique challenges of operationalizing foundation models."

James Chen, CTO, AI Solutions Provider

"The skills gap in LLMOps is severe. Engineers who can properly deploy, scale, and monitor foundation models are commanding top-tier compensation packages because they directly impact our ability to monetize AI investments."

Maya Johnson, LLMOps Engineer

"This course filled the exact knowledge gap I had between traditional MLOps and the specialized needs of LLM systems. Within weeks of completion, I was leading our company's RAG implementation, which led to a $45K salary increase."

Course Schedule

Next Session Starting On

August 11th 2025, 7PM EST


Your Time Commitment

  • Monday, Wednesday, Friday: 7PM-9PM
  • Saturday: 9AM-12:30PM (with 30-min break)
  •   Plus 5-10 hours of weekly project work



Online Live Instructor Lead Training Session for Job-Ready LLMOps


Curriculum Overview

  Week 1: LLMOps Foundations
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  Week 2: Data Engineering for Foundation Models
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  Week 3: LLM Development Lifecycle
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  Week 4: CI/CD for LLM Applications
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  Week 5: Deployment Strategies
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  Week 6: Monitoring & Continuous Improvement
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  Week 7: Security and Advanced Topics
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  Week 8: Capstone Project
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Choose a Pricing Option

Tuition Investment, ROI, & Timings


Tuition: 

Regular: $1,399 (Payment plans are available upon approval)

Student Price: $999 (Payment plans are available upon approval)

ROI:

Graduates report an average salary increase of $32,000 within six months of completion. Companies typically spend $15,000+ to recruit specialized AI engineers—your new skills make you an immediate asset.

Time Commitment:

  • Monday, Wednesday, Friday: 7PM-9PM
  • Saturday: 9AM-12:30PM (with 30-min break)
  • Plus 5-10 hours of weekly project work

Your Instructor

Dr. Vijay Boppana, Ph.D.

With experience spans both traditional MLOps and the emerging field of LLMOps engineering. He has designed and implemented production systems for foundation models at scale, giving him firsthand insight into the unique challenges of operationalizing LLMs and agentic AI.


Limited Enrollment — Secure Your Spot

This highly specialized program is limited to 20 participants to ensure hands-on guidance and high-quality learning outcomes. Previous cohorts have filled within days of opening due to the massive demand for LLMOps expertise.

Apply now and join the elite tier of AI infrastructure engineers.

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Frequently Asked Questions

How does LLMOps differ from traditional MLOps?

LLMOps addresses the unique challenges of operationalizing foundation models and agentic AI systems:

  • Different evaluation metrics (beyond accuracy)
  • Specialized infrastructure for RAG systems and vector databases
  • Multi-modal model deployment
  • Prompt management and versioning
  • Agent monitoring and safety guardrails

What technical prerequisites are required?

Participants should have:

  • Experience with Python programming
  • Familiarity with cloud services (AWS, GCP, or Azure)
  • Basic understanding of machine learning concepts
  • Experience with containerization and CI/CD pipelines
  • Some exposure to LLMs (e.g., using ChatGPT API)

Is this course suitable for MLOps professionals?

Yes! If you already work in MLOps and want to specialize in foundation models, this course provides the specialized knowledge you need to transition to LLMOps roles, which typically command higher compensation.

Will I build real production systems?

Absolutely. The capstone project involves building a complete agentic RAG system with production-grade infrastructure, giving you a substantial portfolio piece to demonstrate your capabilities to employers.