AI & Machine Learning
Top-demand skill globally, including in remote roles available from Georgia.
Why learn this
AI / ML is the loudest hiring category in tech right now, but the entry bar is real: "prompt engineering" alone won't land a serious role. What pays well is the engineering layer around models — data pipelines, fine-tuning, evaluation, MLOps, putting LLMs into production behind real APIs. From Tbilisi, the highest-leverage path is the "AI engineer" profile: a competent backend engineer who can also wire up an LLM and ship it to production. Pure research roles are scarce and remote-unfriendly.
- Global 2025
AI tools tracked at scale among professional developers globally
See AI section →Source: Stack Overflow Developer Survey
- Global 2025
AI / ML repository activity tracked in GitHub Octoverse
See Octoverse →Source: GitHub Octoverse
- Tbilisi 2026
Active AI / ML / data listings on the largest Tbilisi job board
Browse current listings →Source: jobs.ge
Where it's used
Recommendation systems, fraud detection, content moderation, document understanding, and now — broadly — LLM-powered features bolted onto every SaaS that wants to look modern. The interesting Tbilisi-accessible work is in the "applied AI" bucket: shipping LLM features into existing products at international companies that hire remote. Pure-research roles are rare even in larger markets.
What recruiters call this role
Common job titles
- ML Engineer
- AI Engineer
- Data Scientist
- Applied Scientist
- MLOps Engineer
- AI Solutions Engineer (LLM)
Pairs well with
Our courses
Ordered from beginner to advanced — pick the entry point that matches where you are now.
Introduction to AI: UI Generation with Copilot
Learn how to use AI tools—especially GitHub Copilot—to generate modern UI layouts, components, styles and complete website structures. A practical course for developers who want to speed up front-end development using AI.
AI-Powered .NET Development
Integrate AI into your .NET applications using OpenAI and Azure OpenAI APIs. Build intelligent features: chat, summarization, embeddings, semantic search, and RAG pipelines — all in C# and ASP.NET Core.
Prompt Engineering & AI Workflow Automation
Learn to work effectively with AI models: write high-quality prompts, build automated workflows using Cursor, Copilot, and API tools, and boost your daily development productivity 10x.
Building LLM-Powered Apps: RAG & Agents
Build production-grade AI applications using large language models. Cover vector databases, retrieval-augmented generation (RAG), autonomous agents, tool use, evaluation, and deployment patterns.
AWS Bedrock & AI Services for Developers
Deploy and use AI models on AWS: Bedrock (Claude, Llama, Titan), Lambda, API Gateway, S3, and DynamoDB. Build enterprise AI solutions integrated with your existing backend stack.
Building MCP Servers & AI Tool Integrations
Master the Model Context Protocol (MCP) — Anthropic's open standard for connecting AI models to external tools and data. Build custom MCP servers, expose APIs to Claude, and architect next-gen AI integrations.
Building Agents with the Claude Agent SDK
Design and ship custom AI agents with the Claude Agent SDK. Build agent loops, define tools, manage memory and sub-agents, evaluate behavior, and deploy multi-agent systems that solve real engineering tasks autonomously.
Spec-Driven Development Foundations: From Philosophy to Operating Model
Learn to write specs that agents actually obey, ship code as a cache of a durable spec, and operate the spec→context→evals trinity on real codebases. Vendor-agnostic, tool-agnostic, brownfield-ready — the methodology course that pairs with any agentic stack.
OpenSpec Mastery: Production Spec-Driven Workflows for AI Coding Agents
Operationalize SDD with OpenSpec — the open-source spec framework that treats specs the way Git treats code. Master /opsx:propose, /opsx:apply, and /opsx:archive on a real brownfield codebase. CI gates, multi-engineer collaboration, retrofitting legacy specs, and the workflow rituals that make it stick.
From our blog
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The AI Tools Every Developer Should Be Using in 2025
Not a hype list. A practical breakdown of which AI tools actually save time, which ones waste it, and how to build workflows that make you measurably faster.
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Claude Code in Production: What I Learned in 6 Months
Six months of using Claude Code as a daily-driver tool — the workflows that actually save time, the ones that quietly waste it, and the configuration most teams never set up.
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Context Engineering: The Discipline That's Replacing Prompt Engineering in 2026
Prompt engineering was never the real skill. After two years of shipping AI features in production, the discipline that actually moves the needle is context engineering — state, tools, retrieval, history, and constraints assembled into the model's window at the right moment. Here's the senior-engineer's frame.
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From C# to AI Agents: A .NET Developer's Path to Building with Claude
You already know C#, ASP.NET Core, and how to ship production backends. Here's how to reuse those skills to build serious AI agents with Claude — without throwing away your stack.
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Durable Execution for Agents: The Fifth Discipline Your .NET Background Already Prepared You For
Spec is the truth. Context is the assembly. Evals are the proof. OpenSpec is the operating system. The substrate underneath — the thing that keeps a multi-step agent alive across retries, restarts, and the human-in-the-loop who walks away from their desk at 5pm — is durable execution. Every .NET engineer who's ever shipped a MassTransit saga already understands the pattern; here's how it maps onto serious AI agents in 2026, and why it's the fifth discipline that finally closes the arc.
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Evals in 2026: The Test Suite for Systems That Aren't Deterministic
Your AI feature worked yesterday and fails today. No code change, no prompt change, no model change. That's what life without evals looks like. This is the third leg of the spec → context → evals trinity — and the discipline most teams skip.