SwirlAI

Cohort 6

Cohort 3

Cohort 4

Cohort 1

Cohort 5

Master Cohort

Join group of AI engineers pioneering the journey from prototypes to production-ready AI solutions. 

Cohort Alpha

Cohort Beta

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Building, training, and deploying machine learning models — from classical ML to deep learning.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

Master end-to-end AI engineering - transform prototypes into production-ready apps with LLMs, RAG & agents in just 8 weeks.

Founder @ SwirlAI • Ex CPO @ neptune.ai

Aurimas Griciunas

LinkedIn Top Voice in AI • Founder & CEO @ SwirlAI

End-to-End AI Engineering Bootcamp

Lead Solutions Engineer

Automation Engineer

Software Developer

Head of AI and Data

Software Engineer

Software engineer

Technical Lead

Senior Consultant

Senior ML Engineer

neptune.ai

The End-to-End AI Engineering Bootcamp is an 8-week, cohort-based experience designed to turn technical professionals into full-stack AI engineers who can confidently design, build, and deploy production-grade AI systems.🛠️ What You’ll BuildYou’ll develop your own capstone project - a real-world AI application built sprint by sprint, applying each week’s concept to solve a business-relevant use case. By the end, you’ll present it live on Demo Day, with a working repo and deployed app you can showcase to hiring managers, CTOs, or investors. 🧑‍💻Technologies include:<ul><li>LLM APIs (Gemini, Claude, GPT, etc.).</li><li>Vector databases &amp; RAG.</li><li>AI agent libraries (LangChain, LangGraph, ADK, OpenAI Agents SDK).</li><li>Docker, FastAPI, Kubernetes, cloud deployment.</li><li>Observability, evaluation, and performance testing.</li><li>Communication protocols (A2A, MCP).</li></ul>🧠 How It WorksEach week follows a real engineering sprint:<ul><li>Sprint Lesson (Monday): Self-paced learning with videos, cheatsheets &amp; reference code.</li><li>Sprint Review (Tuesday): Live walkthrough with Aurimas + deep Q&amp;A.</li><li>Sprint Build Lab (Thursday): Live coding session to implement sprint features.</li><li>Bonus QnA and Feedback sessions.</li></ul>🎬 Pre-course that will help you prepare <a target="_blank" rel="noopener noreferrer nofollow" href="https://youtube.com/playlist?list=PLeId95Z-0iyOATuFuHt3zP5enX3eCOsoq&amp;si=1xrntQdNOUkXwH6H">here</a>.

🚀 Build Real AI Products, Not Just Prototypes

Design and optimize RAG architectures

Engineer and orchestrate agentic systems

Design and deploy multi-agent systems for complex workflows

Implement structured prompt and context management

Apply LLMOps for observability and continuous evaluation

Build and deploy production-grade GenAI applications

Software Engineers &amp; DevelopersWho want to transition into AI Engineering and build, deploy and scale real-world AI systems end-to-end.

ML Engineers &amp; Data ScientistsWho want to architect, evaluate and scale production-grade agentic AI systems.

Technical Founders &amp; CTOsWho want to make AI a core product capability and confidently design and ship scalable AI systems.

Master the operational layer most AI courses skip: evals, fine-tuning, serving, cost control, and production reliability.

Aurimas Griciūnas

LLMOps Mastery is a 6-week, cohort-based deep dive for engineers who have built LLM applications and need to make them production-grade. You will optimize, fine-tune, deploy, and operate real LLM systems.🛠️ What You'll OptimizeYou start with three pre-built AI systems and spend 6 weeks transforming them: adding eval gates, observability, fine-tuned models, optimized serving, and cost controls.🧑‍💻 Technologies include:<ul><li>Evaluation frameworks and LLM-as-judge pipelines</li><li>Observability and tracing</li><li>Automated prompt optimization</li><li>LLM gateways and model routing</li><li>Fine-tuning</li><li>Inference serving and quantization</li><li>GPU profiling and multi-model serving</li><li>CI/CD with eval gates</li></ul>🧠 How It WorksEach week covers 5 lessons with heavy hands-on:<ul><li>Live Sessions (2x/week, 90 min): Concepts, real-world trade-offs, Q&amp;A.</li><li>Written Content: Detailed technical writeups for reference.</li><li>Hands-On Labs (4-7 hrs/week): Deploy serving engines, fine-tune models, stress test under load, and profile GPUs.</li></ul>

⚙️ LLMOps: From Eval Pipelines to Optimized Inference

Build evaluation pipelines that catch regressions before production

Instrument and monitor LLM systems with production-grade observability

Optimize prompts systematically and implement cost-based routing

Fine-tune and align models for production workloads

Deploy and optimize LLM inference for speed, cost, and scale

Architect and operate production LLM systems end-to-end

Platform EngineersWho run LLM systems in production and need to cut cost, improve reliability, and scale serving.

ML &amp; AI EngineersWho have built LLM apps and want to master evals, fine-tuning, inference serving, and cost optimization.

Engineering Managers &amp; Tech LeadsWho make AI infrastructure decisions and need real data on self-hosting vs API and GPU spend.

Learn directly from SwirlAI

SwirlAI

Courses

End-to-End AI Engineering Bootcamp

LLMOps Mastery

Lightning Lessons

From AI Demo to Deployed App

Make Your Agents Trustworthy: Evals for the Super IC

State of Context Engineering in 2026

Building Real AI Engineering Experience

Getting Hired As AI Engineer in 2026

AI Engineering in Practice: Role, Skills, Industry Reality.