Building Multi-modal Agents
Master the frontier of AI: Agents that see, hear, and act. Learn to orchestrate VLMs, Audio loops, and UI navigation.
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About this program
The next wave of AI isn't just text. It's vision, audio, and spatial reasoning. In this advanced cohort, you will learn to build agents that can navigate UIs, analyze complex documents/images, and interact via voice. You'll work with vision-language models (VLMs), speech-to-text-to-speech (STTS) loops, and multi-modal RAG systems.
Who is this for?
Senior AI Engineers, Product Architects, Research Engineers
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Live Engineering
2-3 hrs / week
Deep-dive interactive technical sessions focusing on architecture, code walkthroughs, and edge cases. Fully recorded.
Independent Build
4-6 hrs / week
Asynchronous reading materials, implementing weekly milestones, and collaborating via Discord for unblocking code errors.
Weekly Syllabus
Each week is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward the final capstone.
8 weeks of intensive multi-modal engineering
A fully functional multi-modal sovereign assistant
Technical implementation labs and cross-modal system architecture review
The Multi-modal Sovereign Era
- We move beyond tokens into pixels and waves.
- You will explore the architecture of Vision-Language Models (VLMs) and multi-modal encoders.
- You'll understand fixed vs.
- dynamic resolution encoding and implement basic cross-modal reasoning loops that treat images as first-class citizens in the prompt stream.
Understand the core architecture of multi-modal systems.
A cross-modal reasoning script using GPT-4o and Claude 3.5.
Visual Prompting & Spatial Logic
- Seeing is different from understanding.
- We teach agents to 'look' at specific coordinates.
- You will implement Visual Referring (Set-of-Mark) techniques to identify UI elements and perform spatial reasoning across complex diagrams, multi-page PDFs, and live camera streams.
Extract structured data and spatial relationships from visual inputs.
A spatial reasoning agent capable of diagram analysis.
Multi-modal RAG: Images as Context
- Standard RAG is for text; we build for everything.
- You will implement pipelines that index images, charts, and infographics via multi-modal embeddings.
- We'll explore COLBERT-style late-interaction models for extremely high-precision visual retrieval from massive unstructured document sets.
Build a retrieval system that 'understands' charts and imagery.
A multi-modal RAG engine with visual document retrieval.
Audio Agents: The Voice-Native Loop
- Real-time agents must hear and speak.
- We'll architect low-latency loops using Whisper for STT (Speech-to-Text), streaming LLM generation, and sophisticated TTS (Text-to-Speech) engines.
- You'll learn to handle interruptions, manage conversational state, and optimize to stay under 500ms total latency.
Construct a low-latency conversational voice agent.
A working voice-enabled agentic loop with interruption handling.
Autonomous UI Navigation (GUI Agents)
- The browser is the ultimate agent tool.
- We implement the 'Vision-as-Action' paradigm, where agents navigate GUIs entirely via visual feedback.
- You'll build agents that can interpret screen states, calculate click coordinates, and perform multi-step web navigation tasks to automate browser workflows.
Build an agent that navigates the web like a human user.
A visual web-navigating agent fulfilling a complex task.
Multi-modal State & Memory Management
- Complex agents need a persistent understanding of the world.
- We'll design state machines that track visual, audio, and text history simultaneously.
- You'll learn to handle 'Modal Conflicts' (what if the user says one thing but the screen shows another?) and implement cross-modal memory retention.
Synchronize visual and textual context across long sessions.
A state-managed agent handling multi-modal conflicts.
Fine-tuning VLMs with LoRA
- Sometimes base models are blind to specific domains.
- We'll perform LoRA (Low-Rank Adaptation) fine-tuning on open-source VLMs like Moondream or LLaVA.
- You'll teach a model to identify specific industrial parts, medical artifacts, or custom enterprise dashboard states with high precision.
Specialize a vision-language model for a custom domain.
A domain-specialized VLM checkpoint trained via LoRA.
Capstone: The Multi-modal Sovereign
- The final mission.
- You will architect and deploy a multi-modal agent that combines vision, voice, and action.
- This capstone will demonstrate the engine's ability to 'Observe' a state, 'Listen' to a command, and 'Execute' a complex multi-step cross-platform task autonomously.
Deploy a production-ready, multi-modal sovereign assistant.
A complete multi-modal agent demo and system architecture report.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersEngineering Trust
Our alumni don't just 'use' AI. They architect the core infrastructure at forward-thinking engineering labs. This is a high-trust collective of senior talent.
"We've created a zero-noise environment for senior talent. This is where staff and principal engineers from Silicon Valley and beyond come to cross-pollinate their knowledge of agentic systems and distributed training."
The most technically rigorous program I've attended. No fluff, just pure architectural deep-dives into transformer blocks and swarm logic. This isn't just about calling APIs; it's about understanding the stochastic internals of LLMs.
LangGraph and Multi-agent orchestration was the missing link for our production pipeline. Highly recommended for senior devs who need to move beyond single-prompt engineering into complex, stateful workflows.
Direct 1:1 access to instructors who are actually shipping AI products. The focus on evaluations and evals-driven-dev is unique. We've implemented their RAG evaluation pipeline for our entire stealth startup.
Lead Instructor
Deep pedagogical philosophy balanced with production engineering rigor.
Meet
Anubhav
Anubhav is an AI solutions and engineering leader with two decades of global experience executing machine learning, generative AI, and physical intelligence initiatives.
With a proven track record of founding startups and building 0-to-1 engineering teams, he has architected and delivered production-grade systems across B2B SaaS, industrial robotics, sports tech, and massive-scale consumer streaming platforms serving over 600 million users.
At skilling academy, he personally mentors every student, bringing extensive experience in enterprise strategy, multi-agent workflows, computer vision, and scalable distributed architectures from the boardroom to the IDE.
Technical Expertise
- Transformers / Attention
- GNNs & Graph Search
- RLHF / DPO Alignment
- Distributed Training
- vLLM / NVIDIA Triton
- Kubernetes / Ray
- VectorDB Scaling
- Hybrid Retrieval
- Knowledge Graphs
- Autonomous Execution
- ReAct / Tool-use
- Planner Architectures
System FAQ
Addressing technical edge cases and curriculum logistics for the committed engineer.
Our cohorts are crafted for mid-to-senior level software engineers, data scientists, and technical product managers who are comfortable with Python and basic web architecture. If you've been 'prompt engineering' but want to understand the underlying mechanics—transformer blocks, vector algebra, and autonomous agent orchestration—this is for you.
Plan for 6-8 hours of focused effort per week. This breaks down into 2 hours of live, interactive deep-dives on Saturdays, 1 hour of midweek Q&A/Office Hours, and 3-5 hours of dedicated hands-on project implementation where you'll build production-ready AI modules.
Life happens. Every live session is recorded in 4K and uploaded to our private portal within 2 hours. You'll have lifetime access to these recordings, including all updated versions of the curriculum. Our Discord community and mentors are active 24/7 to help you get back on track.
Not necessarily. While we discuss hardware optimization, most of our practical work utilizes cloud-based environments (Google Colab, Modal, or Lambda Labs). We provide credits and setup guides so you can run large-scale inference and fine-tuning without burning through your own hardware.
We keep cohorts focused (max 60) to maintain a high mentor-to-student ratio. You’ll be split into smaller review pods, and you’ll get dedicated feedback via office hours and code review workflows. This keeps discussions high-bandwidth and practical.
We teach 'First Principles'. While we use popular frameworks for speed, we spend significant time building core components (like Custom RAG retrievers or ReAct loops) from scratch. This ensures that when the next big framework arrives, you'll understand exactly how it works under the hood.
Absolutely. Our final project is a portfolio-grade AI system that solves a real business problem. We also provide a dedicated session on the AI Engineering interview landscape, resume reviews for technical roles, and introductions to our network of hiring partners in the AI space.
We want you to be 100% satisfied. If after the first week you feel the cohort isn't the right fit, we offer a full, no-questions-asked refund. Our goal is to build a community of committed builders, and we stand by the quality of our curriculum.
Yes. All students get lifetime access to our internal repository of production-ready templates, deployment scripts, and evaluation benchmarks. These are the same tools our instructors use to build and scale AI solutions in their day-to-day professional work.
Upon successful submission and review of your final 3 project modules, you will receive a cryptographically signed digital certificate. This certificate is recognized by our network of partner companies and can be directly shared on LinkedIn or included in your professional portfolio.