Tayana Academy — For Builders

How Builders works

One pathway, on Orion.

Everyone enters through Orion — our LMS — and follows the same four steps. Where you go next depends on what you already know, not where you started.

1

Enrol

Join Orion

2

Groundwork

AI Foundation 1 & 2

3

Direction

Career Assessment

4

Placement

Technical Screening

Python pathPython required

Routes into Pro-Code Agent Engineers and Machine Learning Engineers.

Python Assessment

PassEligible for Pro-Code Agent Engineers and Machine Learning Engineers.

Not yetPython Foundation — from basics to real-world skills.

Re-assess anytime — retry until you're ready

Full-Stack pathFull-stack required

Routes into AI Engineer — LLM apps and AI products, deployed.

Full-Stack Assessment

PassEligible for the AI Engineer track.

Not yetFull-Stack Foundation — frontend, backend, databases & APIs.

Re-assess anytime — retry until you're ready

No technical background? Start with a Foundation course, then re-assess whenever you're ready.Python FoundationFull-Stack Foundation

Select a path to see the tracks it unlocks

Where the pathway leads

Four AI career tracks.

Screening places you where you'll actually succeed — no guessing which track fits. Every track ends the same way: something shipped. An agent, a product, or an end-to-end ML system.

Orion AI career track · 01

No-Code Agent Engineers

Job-ready. Tool-powered. Business-focused.Entry · AI Foundation + Career Assessment

For operators, analysts and founders who want agents working for them without writing code. Most non-technical Builders start here. Entry is AI Foundation plus the Career Assessment — no technical screening.

No coding requiredEntry · AI Foundation + Career Assessment4 weeks · live24 seats

You walk away withA production agent your team runs weekly — built visually, documented, handed over

Week 1Visual builders & the agent landscape. Your first automated workflow live by Friday.

Week 2Templates, triggers & integrations. Connect the tools your team already runs.

CapstoneA working agent, reviewed live by your mentor.

Week 3Multi-step agents. Error handling and human-in-the-loop checkpoints.

Week 4Harden, document, hand over. Demo day.

ChatGPTClaudeGeminin8nMakeZapierFlowiseLangflowAirtableNotion AI

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Orion AI career track · 02

Pro-Code Agent Engineers

Job-ready. Engineering-focused. Enterprise-ready.Entry · Python Assessment / Python Foundation

For engineers who want to design intelligent multi-agent systems in code. Unlocked by the Python path.

Python required6 weeks · live24 seats

You walk away withA deployed multi-agent system handling a real business intent — routed, recovered, evaluated

Week 1Python refresh, LLM fundamentals and prompt engineering.

Week 2LangChain & tool calling. Your first agent handles a real intent.

Week 3Multi-agent systems with CrewAI and LangGraph — routing and recovery.

CapstoneA multi-agent workflow reviewed end to end.

Week 4MCP & integrations. Agents that reach into real systems.

Week 5Evals, guardrails & observability.

Week 6Deploy, measure, present. Demo day.

PythonVS CodeGit & GitHubLangChainCrewAILangGraphFastAPIDockerPostman

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Orion AI career track · 03

AI Engineer

Full-stack. AI-native. Production-ready.Entry · Full Stack Assessment / Full Stack Foundation

For full-stack developers becoming AI product engineers. Unlocked by the Full-Stack path.

Full-stack required6 weeks · live24 seats

You walk away withAn LLM product deployed to production — RAG, agents and evals behind a real interface

Week 1Foundations & the frontier. The model landscape in production today.

Week 2RAG on private data. Retrieval, chunking, embeddings, citations.

Week 3Agents & tool use inside a full-stack app.

CapstoneYour product's core loop, shipped and reviewed.

Week 4Evals & red-teaming. Release gates you can trust.

Week 5Deploy & observe. Latency, cost and monitoring.

Week 6Polish & present. Demo day.

ReactNext.jsNode.jsFastAPIPostgreSQLPineconeLangChainOpenAIDocker

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Orion AI career track · 04

Machine Learning Engineers

Data-driven. Model-centric. Impactful.Entry · Python Assessment / Python Foundation

For Python engineers going deep — training, evaluating and operating models end to end. Unlocked by the Python path.

Python required6 weeks · live24 seats

You walk away withAn end-to-end ML system — trained, evaluated, served and monitored

Week 1Problem framing & data pipelines. A clean training set.

Week 2Model training & evaluation. Baselines before cleverness.

Week 3Iteration & error analysis. Beat the baseline honestly.

CapstoneA trained, evaluated model reviewed against the brief.

Week 4End-to-end systems. Serving, batch and real-time paths.

Week 5MLOps. Versioning, monitoring and retraining loops.

Week 6Ship the system. Demo day.

PythonNumPyPandasScikit-learnTensorFlowPyTorchMLflowHugging FaceDocker

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See full week-by-week Compare both pillars

The journey, week by week

Every track, mapped to the day.

Open your track. Each week ends with something shipped, and every track carries a mid-cohort capstone checkpoint before demo day.

Week 1 — Use cases, mapped and scored. A mapped process and a scored shortlist, with one use case chosen alongside your mentor.
Week 2 — Your first workflow, live. One automated workflow running on a schedule, against live data.
Mid-cohort capstone — Mid-cohort review. A reviewed agent and a plan to harden it.
Week 3 — Agents that decide. A multi-step agent handling a real business intent, with a human checkpoint.
Week 4 — Harden, document, hand over. A production agent, a handover pack and a demo-day presentation.

Full week-by-week for this track

Week 1 — Python, OOP and the engineering baseline. A scaffolded repository and your first validated, typed LLM call.
Week 2 — Tool calling and function execution. An agent completing a real task through two or more tools.
Week 3 — Multi-agent systems. A multi-agent workflow handling a business intent end to end.
Mid-cohort capstone — Mid-cohort review. A reviewed multi-agent system and a hardening plan.
Week 4 — MCP and backend integration. Your agent exposed as an API and reaching a real system through MCP.
Week 5 — Evals, guardrails and observability. An eval suite your system has to pass before release.
Week 6 — Deploy and demo day. A deployed multi-agent system with its eval report and runbook.

Full week-by-week for this track

Week 1 — AI product architecture and the frontier. A running application shell returning its first grounded LLM response.
Week 2 — Retrieval-augmented generation on private data. A RAG pipeline answering questions on your own data, with citations.
Week 3 — Agents and tool use inside the product. Your product's core loop, working end to end.
Mid-cohort capstone — Mid-cohort review. A reviewed product loop and a plan to production.
Week 4 — Model evaluation and red-teaming. An eval harness and a documented security pass on your product.
Week 5 — Deploy and observe. Your product deployed, instrumented and measured.
Week 6 — Production hardening and demo day. A deployed AI product with its architecture write-up.

Full week-by-week for this track

Week 1 — Problem framing, statistics and data pipelines. A clean, documented training set and a stated success metric.
Week 2 — Feature engineering and classical machine learning. A trained baseline model that beats a naive benchmark, with results you can defend.
Week 3 — Deep learning across vision and language. A trained deep-learning model with a documented error analysis.
Mid-cohort capstone — Mid-cohort review. A reviewed model and a plan to production.
Week 4 — Experiment tracking and model management. Your experiments tracked and your best model registered with a model card.
Week 5 — Serving and MLOps. Your model served behind an endpoint, with monitoring in place.
Week 6 — End-to-end system and demo day. An end-to-end ML system with its model card and runbook.

Full week-by-week for this track

Choose your stack

Code, or no-code.

No-Code Agent Engineers opens straight after AI Foundation and the Career Assessment. The three technical tracks are routed by technical screening, and each path builds on its own stack. No-code operators ship with visual builders; Python and Full-Stack tracks ship in code — same outcome, a deployed system.

No coding required

No-Code path

No-Code Agent Engineers. For operators, founders and PMs.

Build & workflown8n · visual builders

InterfaceLovable · Bolt · Framer

Logic & APIsn8n · webhooks · no-code connectors

LLM & inferenceHosted model APIs · LM Studio

RAG & dataFile search · NotebookLM

DatabasesAirtable · Google Sheets · Supabase

Agentsn8n AI · Langflow · agent builders

DeploymentOne-click hosting

Python or Full-Stack required

Code path

AI Engineer, Pro-Code Agent Engineers & Machine Learning Engineers. For engineers.

Build & workflowPython · TypeScript

InterfaceReact · Streamlit

Logic & APIsFastAPI · Pydantic

LLM & inferenceOpenAI · Anthropic · Ollama

RAG & dataLlamaIndex · pgvector · vector DBs

DatabasesPostgres · Supabase

AgentsLangChain · CrewAI · LangGraph · MCP

DeploymentCloud · containers · CI/CD

Not sure which is yours? The Career Assessment points you at a track, and Technical Screening places you on the Python or Full-Stack path — and Foundation courses get you ready for either.

Engineer your AI career.

One pathway, on Orion.

Four AI career tracks.

No-Code Agent Engineers

Pro-Code Agent Engineers

AI Engineer

Machine Learning Engineers

Every track, mapped to the day.

Code, or no-code.

Ready to build?