Build & Learn: From Data Science to AI Engineering Week 1
Event PAST

Build & Learn: From Data Science to AI Engineering Week 1

15 August Online

About this event

📅 Week 1: Kickoff — From Data Science to AI Engineering
After a short break, Build & Learn is back with a new seven-week focus:
learning how to build and ship real AI applications. 👩‍💻

AI engineering is becoming an increasingly important path for data scientists, analysts and Python developers. The biggest shift is not simply learning more machine learning theory—it is learning how to turn an AI prototype into a working application that other people can use.

Over seven weeks, we will build AND deploy basic end to end LLM/AI project touching key technologies ( FastAPI, Postgres, Docker, a real AI pipeline. )

  • Week 1 — Kickoff & Setup: Set up Python, GitHub, Docker, API keys, and run the starter project locally.
  • Week 2 — Learn the LLM specific layer. (OpenAI API)
  • Week 3 — Build production ready AI backends. (Fast API, MCP server)
  • Week 4 — Connect AI to your data with RAG (vector database)
  • Week 5 — Lean into evals and observability (tracing, evaluation)
  • Week 6 — Build & Ship Your Version
  • Week 7 — Demo Day

By the end of the 7 weeks you will have a working AI application in your own GitHub repository and experience building an AI pipeline, backend, database and frontend.

✨ Who is this for?
This cycle is designed for data scientists, analysts, software developers and anyone interested in transitioning toward AI engineering.
No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation before the first build session.

✨ Who’s hosting?
I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products.
I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and about moving beyond AI hype toward applications that actually work.

💻 Bring: Your laptop and curiosity. Please also grab ☕ at the octopus bar to support their business and providing the location!

Want to find AI jobs?

Join thousands of AI professionals finding their next opportunity

We respect your inbox. Unsubscribe at any time.

© 2026 DevFound.ai. All rights reserved.

Common Questions

Frequently asked questions

Quick answers about how DevFound's AI matching, resumes, and referrals work.

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.