This 9-week, project-based certification teaches you the core skills employers expect from LLM engineers: dataset preparation, LoRA/QLoRA, evaluation, model optimization, and scalable deployment.
Earn the Certified LLM Engineer certificate and build two portfolio-ready projects that demonstrate your hands-on expertise.

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Why This Program Matters

LLMs have moved beyond prompting — they now require engineering expertise.
This certification program bridges the gap between experimentation and production by teaching how to fine-tune, evaluate, and deploy open-source models in real environments.
You’ll learn techniques used by modern AI teams to customize and serve LLMs efficiently, reliably, and cost-effectively.

Your proof of learning isn’t just a certificate — it’s two portfolio-grade projects demonstrating your ability to adapt, optimize, and deploy models for real-world use cases.

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What You'll Learn

Develop the technical capabilities that modern AI teams depend on. By the end of this program, you'll be able to:

• Understand the LLM landscape, benchmarks, and how to choose the right base model
• Prepare and preprocess datasets for instruction and domain-specific fine-tuning
• Apply precision and quantization strategies to optimize training memory and performance
• Implement parameter-efficient fine-tuning with LoRA and QLoRA
• Train models using Hugging Face Transformers, PEFT, and Accelerate
• Manage experiments and reproducibility using Weights & Biases and Axolotl
• Scale fine-tuning across single and multi-GPU environments with DeepSpeed ZeRO
• Evaluate and compare models using lm-evaluation-harness, Giskard, and custom tests
• Optimize models through merging (MergeKit) and advanced quantization (bitsandbytes, GGUF)
• Deploy and serve models efficiently with vLLM, Modal, SageMaker, and Bedrock
• Monitor, secure, and govern production LLM systems using CloudWatch and LangSmith

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What You'll Build

Each phase of the program centers on hands-on work designed to strengthen your skills and build portfolio-grade demonstrations of real-world LLM engineering.

Capstone Project 1: Model Fine-Tuning
Select a use case (chatbot, QA system, summarizer, or domain-specific assistant), prepare a custom dataset, and fine-tune an LLM end-to-end. Document your approach with training metrics, loss curves, and qualitative evaluation.

Capstone Project 2: Model Deployment
Deploy your fine-tuned model using two methods (e.g., vLLM locally and cloud API). Run systematic tests on latency, cost, and reliability. Deliver a working demo or hosted API with complete deployment documentation.

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What You'll Earn

Every project you complete earns more than just experience — it builds a verified record of your technical abilities in LLM engineering.

Certified LLM Engineer Certificate
Your primary credential demonstrating expertise in fine-tuning, evaluating, and deploying large language models using production-grade tools and workflows.

2 Micro-Certificates
Earn a certificate for each module as you progress through the program:

• LLM Fine-Tuning Specialist — Fine-tune and optimize an LLM using PEFT techniques
• LLM Deployment Engineer — Deploy a production-grade fine-tuned LLM with vLLM and cloud endpoints

3 Digital Badges
Showcase your achievements on LinkedIn and professional platforms with shareable, verifiable badges.

2 Portfolio Projects
Publish your completed capstone projects on Ready Tensor — each with working code, documentation, training results, and deployment demos visible to employers and peers.

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Who This Program Is For

This certification is built for technical professionals who are ready to go beyond prompting and orchestration to work directly at the model layer. If you want to shape LLM behavior through fine-tuning, evaluation, and optimized deployment — this is for you.

Ideal for:

• AI/ML Engineers and Applied Researchers looking to specialize in LLM customization
• NLP Developers who want hands-on experience with modern fine-tuning techniques
• MLOps Practitioners seeking to add LLM deployment and optimization skills
• Data Scientists building LLM-enhanced tools for specific domains or workflows
• Agentic AI Developer Certification graduates ready to dive deeper into the model layer

⚠️ Note: This is an advanced, technical program. Strong Python skills and prior experience with LLMs are required.

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Program Prerequisites

Before you enroll, make sure you have the foundational technical skills to hit the ground running. This program assumes you're already comfortable working with LLMs and writing production-quality Python code.

Required Skills:

• Intermediate Python programming (functions, classes, modules)
• Familiarity with Hugging Face, PyTorch, or similar ML frameworks
• Experience with LLM APIs and foundational NLP concepts
• Understanding of basic ML workflows (training loops, evaluation, model saving)
• Comfort with command line environments and package management

Recommended (but not required):

• Agentic AI Developer Certification — provides strong preparation in LLM system design and agentic workflows
• Experience with GPU computing or cloud ML platforms (AWS, GCP, Azure)
• Basic experience with deployment tools like FastAPI or Streamlit/Gradio
• Prior exposure to model training or transfer learning

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How to Enroll

This is a paid certification program. Here's how to enroll:

If you don't have a Ready Tensor account yet:

1. Register for a free account on Ready Tensor. Sign up here.
2. Complete a quick onboarding process:
   • Add your name and optional details like location, organization, and short bio.
   • On the "Certification Program" selection page, choose LLM Engineering & Deployment Certification.
3. Complete your enrollment payment and you'll gain access to the Lessons page on October 22nd to begin.

If you already have a Ready Tensor account:

1. Visit the Certifications page from the top navigation menu.
2. Select the LLM Engineering & Deployment Certification card.
3. Click "Enroll Now" in the banner at the top of the page and complete payment to join the program.

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How the Program Works

This is a project-based certification program — you earn your credentials by completing hands-on projects that meet defined technical requirements.

The program is divided into two modules, each culminating in a capstone project:

• Module 1: Fine-Tuning & Optimization
  Focuses on preparing data, applying LoRA/QLoRA, training models efficiently, and documenting fine-tuning results.
  Completing this module earns the Fine-Tuning Specialist micro-certificate.

• Module 2: Deployment & Inference Systems
  Focuses on model evaluation, merging, quantization, and deploying models for real-world use.
  Completing this module earns the LLM Deployment Engineer micro-certificate.

Completing both modules and successfully passing both projects earns the LLM Engineering & Deployment Certificate — your full program credential.

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Learning Format

To complete the projects, you’ll work through structured lessons, code templates, and walkthrough videos.
These are divided across the two modules and arranged in a suggested 9-week learning sequence, but the program is entirely self-paced — you can move faster or slower depending on your schedule.

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Projects and Submissions

You can complete projects individually or in teams of up to 5 members.
Team submissions are encouraged, as they better simulate real-world engineering workflows.

When your project meets the defined technical requirements, you’ll create a submission (called a “publication”) on the Ready Tensor platform.
This includes:

• Your project documentation (the publication)
• A linked code repository

Both the documentation and the repository are reviewed using technical rubrics specific to each project.

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Evaluation Process

• Each project must achieve a minimum score of 70% on the rubric to qualify for certification.
• If a submission doesn’t meet certain requirements, reviewers provide constructive feedback.
• You can revise and resubmit your project after addressing the feedback — the process is iterative, like real software development.
• Once all requirements are met, your project is approved, and your certificate and badge are issued.

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Learn From Industry Experts

This certification is built and led by professionals who work hands-on with LLMs in real-world production environments — not just in research settings.

Abhyuday Desai, Ph.D. — Founder & CEO, Ready Tensor

Abhyuday brings over two decades of experience in AI and enterprise ML, having led large-scale model deployments and infrastructure initiatives for Fortune 500 organizations. At Ready Tensor, he focuses on advancing open-source LLM fine-tuning, evaluation frameworks, and scalable deployment practices.

The program draws directly from current engineering workflows — covering how teams design datasets, apply LoRA/QLoRA, manage GPU efficiency, and deploy optimized models with vLLM and cloud services.

Rather than abstract theory, you’ll gain insights shaped by real deployment scenarios, including the trade-offs, debugging strategies, and optimization techniques used by practitioners shipping LLMs into production every day.

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Build the Skills Real AI Teams Are Hiring For

The future of AI development belongs to engineers who understand the model layer — not just the interface.
This certification equips you with production-relevant LLM skills that make you stand out in the job market.

Enroll in this program and start mastering the tools and techniques behind the next generation of AI systems.

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