SwirlAI

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

Cohort Alpha

Cohort 3

Cohort Beta

Cohort 4

Cohort 1

Cohort 5

Master Cohort

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

Technical Lead

Software Engineer

Senior Consultant

Senior ML Engineer

Senior Data Scientist

Head of CRO & Experimentation

Senior Manager, ML Engineering

Data

Director

CEO / CTO

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.

From AI Demo to Deployed App

Founder @ SwirlAI • Ex CPO @ neptune.ai (Acquired by OpenAI)

See how to take a working AI prototype to a clean, deployable frontend without a full-stack rewrite.

Port a Streamlit prototype or a vibecoded frontend app to v0

Wire backend AI logic to a v0 frontend, push it live, and serve real users without custom infrastructure.

Ship the app to production on Vercel

Use v0 to add a frontend interaction for a backend endpoint your frontend prototype never exposed.

Ship a new UI feature on top of an existing backend

Make Your Agents Trustworthy: Evals for the Super IC

Learn which silent failures break IC agent workflows: hallucinated sources, dropped steps, drifted intent.

Spot the failure modes your agents will hit in production

See one specific failure picked from a working multi-agent system, written into an eval, and resolved end to end.

Catch a real agent failure and fix it live with evals

Keep your agent crew compounding output instead of drifting silently as you ship changes.

Build evals into your agent iteration loop

State of Context Engineering in 2026

Understand the major approaches teams use to give models the right information at the right time.

Core patterns shaping context engineering in 2026

Learn how to compare approaches based on accuracy, latency, token cost, maintainability, and reliability.

Tradeoffs between different context strategies

Use routing, progressive disclosure, and modular capabilities to build agents that are more efficient and reliable.

Apply these patterns to design better agentic systems

Building Real AI Engineering Experience

Founder & CEO @ SwirlAI | Former CPO @ Neptune.ai (acquired by OpenAI)

Learn which parts of the AI lifecycle are most often missing from candidate experience.

Identify Experience Gaps in AI Engineering

Understand how to create projects that surface constraints, failures, and engineering decisions.

Build Systems That Expose Real Trade-Offs

Learn how to present system architecture, evaluation, and iteration in a way that can be assessed.

Make System Work Reviewable by Hiring Teams

Getting Hired As AI Engineer in 2026

Danny Martinez

Co-founder @ Decimals

Learn how demand, competition, automation, and hiring patterns are shaping AI engineering roles today.

Understand the AI Engineering Job Market in 2026

See how automated filters, referrals, and expert networks shape who gets interviewed and who gets missed.

Understand How AI Engineering Candidates Are Screened

Learn how to present real system work, trade-offs, and outcomes in a way that matches hiring expectations.

Position Your Experience for AI Engineering Roles

What End-to-End AI Engineering Really Means

What it means to own an AI system from problem framing through deployment, evaluation, and long-term operation.

End-to-End Ownership of AI Systems

How latency, cost, reliability, data quality, evaluation, and risk shape architecture choices in production AI systems.

Designing AI Systems Under Real Constraints

How AI engineers handle drift, regressions, updates, and failures through evals, monitoring, and controlled iteration.

Learn directly from SwirlAI