Applied AI Engineer
Mastery Program
A Stanford-inspired 7-phase curriculum — from engineering fundamentals to production-ready AI systems. Build real apps, fine-tune LLMs, and deploy enterprise-grade systems.
Production-proven skills the industry is actively hiring for
Go beyond toy demos. Master the engineering patterns used by top AI labs to build scalable, reliable systems.
Production RAG Systems
Design and deploy production-grade RAG systems handling millions of documents with access control and observability.
Autonomous AI Agents
Build multi-agent systems using LangGraph and CrewAI that reason, plan and act on complex real-world tasks.
Fine-Tune LLMs
Adapt foundation models using LoRA/QLoRA on domain-specific datasets. Evaluate, serve, and monitor custom models.
LLM Pipelines
Architect transformer-based LLM pipelines with advanced prompt engineering, CoT reasoning, and context strategies.
AI Systems Deployment
Deploy AI systems to AWS/GCP with Docker, FastAPI, async queues, LangSmith observability, and cost control.
Live Production Portfolio
Graduate with 3+ live, deployed AI applications — demonstrating real engineering judgment to employers.
From zero to production AI engineer in 4 weeks
A 4-week intensive journey designed to transform you into a production-ready AI Engineer. Packed with hands-on labs and real-world enterprise use cases.
Engineering Foundations & AI Basics
Master engineering foundations every AI engineer needs — mathematics, data structures, Python, SQL, cloud platforms, REST APIs, and data pipeline tools. Then accelerate into classical ML, deep learning, neural networks, and the latest generative AI and LLM model providers.
LLM & RAG Mastery
Model Customization & Multi-Agent Systems
Production Deployment & Capstone
Cohort 01
Cohort 01 · 2025
Build real, deployed AI systems
Every project in our curriculum is production-grade. You won't just build toy demos — you'll architect systems that solve real business problems.
Enterprise RAG + Agents Use Case
Multi-source document Q&A with agentic follow-up, citations, and role-based access control. Deployed on AWS.
Autonomous Multi-Agent Workflow Use Case
Orchestrator + specialist agents solving complex multi-step tasks. Real-time monitoring via LangSmith dashboard.
Fine-Tuned Domain AI Assistant
Custom LLM fine-tuned on industry dataset. LoRA adapter training, evaluation pipeline, and production serving.
Multimodal RAG Pipeline
Image, table, and text retrieval from complex documents. PDF layout-aware extraction with ColPali visual embeddings.
AI-Powered REST API Backend
Production FastAPI service with LLM integration, streaming responses, token tracking, and rate limiting.
Phase Mini Projects
Additional hands-on builds across each phase: prompt pipelines, vector search apps, RL reward models, and more.
Internship Opportunities
High-performing graduates will be considered for paid internships at ByteHubble or our network of 50+ hiring partners.
Real Product Experience
Contribute to live RAG pipelines and agent systems used by real enterprise customers.
Fast-track Career
Direct pathways to full-time AI Engineer roles with competitive compensation packages.
Exclusive Network
Access to the ByteHubble alumni network and warm referrals to top-tier tech companies.
Selection Criteria
- Top performers in project evaluations
- Consistent participation in labs
- Minimum 80% attendance rate
Guaranteed Certificate
Every graduate receives a co-branded certificate from JTBI and ByteHubble.
Apply Now →Secure your seat for Cohort 01
Applications are reviewed on a rolling basis. Early applicants receive priority selection.
4 Weeks
Accelerated curriculum
40+ Hours
Live + hands-on
4+ Projects
Portfolio builds
2+ Enterprise Use Cases
Real-world impact
Application Process
Submit Form
Academic and professional details.
Quick Screening
Assessment of engineering skills.
Selection
Receive your offer letter.