Adel Mekkawy

[Google Cloud Tech @GoogleCloudTech]

https://t.co/Nhzbmwl85W

[Ronin @DeRonin_]

How to become AI engineer in next 6 months:

By the end, you want to be able to:

- build LLM apps end-to-end
- use APIs from OpenAI / Anthropic / open-source stacks
- design prompts and context properly
- add tool calling and structured outputs
- deploy real projects

So, let’s discuss your roadmap month by month

Month 1: Get solid enough in coding and fundamentals

What to learn:

- Python really well
- Git + GitHub
- CLI / terminal basics
- JSON, APIs, HTTP, async basics
- basic SQL
- basic data handling with pandas
- virtual environments, package management, error handling
- FastAPI or Flask

Month 2: Master LLM app development

What to learn:

- prompting fundamentals
- system vs user instructions
- structured outputs / JSON schemas
- function/tool calling
- streaming responses
- conversation state
- cost / latency / token basics
- failure handling
- prompt injection awareness

Month 3: Learn RAG properly

What to learn:

- embeddings
- chunking
- vector databases
- metadata filtering
- reranking
- retrieval quality issues
- hallucination reduction
- citations and grounding

Month 4: Agents, tools, workflows, evals

- agent loops
- tool selection
- state management
- retries
- when NOT to use agents
- multi-step workflows
- evaluation harnesses
- task success metrics

Month 5: Deployment, product thinking, and reliability

What to learn:

- FastAPI production patterns
- Docker
- background jobs
- queues
- auth + API key security
- logging
- observability
- prompt/version management
- eval dashboards
- cost monitoring
- rate limits
- caching

Month 6: Specialize and become hireable

these knowledge and skills you gained can be applied in three directions

you need to choose one of them and focus on practice

although everything mentioned above is also best learned purely through practice

Direction 1: AI product engineer

Best if you want startup jobs fast

Focus on:

- LLM apps
- RAG
- agents
- deployment
- product UX

Direction 2: Applied ML / LLM engineer

Focus on:

- fine-tuning
- when to fine-tune vs prompt
- evaluation
- inference optimization
- open-source models
- training pipelines

Direction 3: AI automation engineer

Focus on:

- workflow orchestration
- business process automation
- multi-tool systems
- CRM, docs, email, support, ops use cases

This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work

By month six, you will already have several built products or examples of completed tasks

And it will be much easier to get a job as an AI engineer

Save it so you don't lose it and can return to study later

[Minko Gechev @mgechev]

Angular CLI's MCP server is getting better!

It provides tools to:

‣ Understand your workspace structure
‣ Provide best practices
‣ Look up documentation
‣ See examples

Find how to install it https://t.co/vPFg9MQwyN

[Mike Ducker @EcoSystemMike]

#swassiut has not been about another cute mobile ap or social media site, but solving real problems in #egypt

[Mike Ducker @EcoSystemMike]

2nd recycle company at #swassiut, time to set up a recycle accelerator in #egypt

[Amr AbdelRahman @iAmrRahman]

#swassiut #entrepreneurs I am totally fine to say that I've learned way much more of you guys than what you think you did of us. Hats up!

[ziadaly @ziadaly]

We need Leaders, Inspirational Leaders, lots of them...

#SWAssiut #NewEgypt #Egypt #entrepreneurship #fb