LLMs and model work
the core of AI development -- provider APIs, prompting, fine-tuning and model evaluation. The first thing to master for AI work.
Python and its ecosystem
the primary language for AI development. Learn it alongside web frameworks, async code and data libraries.
Agentic AI and orchestration
frameworks for building AI agents, multi-agent systems and complex pipelines. The hottest direction of 2025-2026.
Databases and storage
relational and NoSQL databases, caching, message queues. You can't build any production service without them.
RAG and vector search
Retrieval-Augmented Generation -- connecting your own data to LLMs. A key pattern for enterprise AI solutions.
DevOps and infrastructure
containerization, CI/CD, cloud, deployment. Needed to ship an AI solution to production.
Classical ML and Data Science
classical machine learning, statistics, data analysis. The foundation the whole AI field rests on.
Self-supervised Learning
1
APIs and integrations
designing and building APIs, integrating with external services, data exchange protocols.
Frontend and web development
client-side development -- React stack, TypeScript, markup. Needed for full-stack AI apps and interfaces.
AI developer tools
AI-enabled IDEs, code generation, tools for AI-assisted development. Speed boost of 2-5x.
Product skills and analytics
product thinking, analytics, A/B tests, pricing. For those who want to influence the product, not just code.
Voice technologies and Computer Vision
specialized AI fields -- voice, speech and image recognition, OCR. Niche but well-paid.
Other programming languages
languages beyond Python/JS. Go is growing for backend AI services, C++ is needed for optimization, the rest come up per project.
MLOps and model monitoring
infrastructure for training, deploying and monitoring ML models in production.
No-code, Low-code and automation
automation without code or with minimal code. A fast way to wire up an AI pipeline without programming.
Architecture and design
design patterns, architectural approaches, microservices. Senior/lead level.
Chatbots and conversational systems
building bots for Telegram and other platforms, NLU, intent classification. The most common "first AI product" at companies.
Data Engineering and data pipelines
ETL, big data processing, Spark, Hadoop ecosystem. For those who work with data at scale.
Methodologies and processes
development management frameworks. The bare minimum for team work.
Document handling and parsing
extracting data from documents, scraping, file handling. A practical task for RAG pipelines.
Testing and QA
automated tests, browser automation, API testing. The footprint for AI projects is still minimal.
GPU and compute optimization
GPU work, CUDA, distributed compute. Needed for training and inference.
Performance Optimization
1
Security and authorization
authentication, access control. A small but important niche.
Mobile development
native mobile development. Rarely seen in AI job postings, but it comes up.
Other
skills that don't form a standalone stack and don't fit logically into other groups.