Microsoft

Started as a Data Scientist and grew to Principal Data Scientist Manager.
My team has supported multiple industries such as retail, gaming, finance, marketing, e-commerce, energy, media, manufacturing, and others. We implement solutions in a wide range of AI areas including recommendation systems, natural language processing, computer vision, time series forecasting, and other machine learning techniques.
Our repository Recommenders ➡️➡️ https://github.com/recommenders-team/recommenders is a 
world reference in recommendation systems. Starred and used by +19k people.

Principal Data Scientist Manager

IE University

I'm helping a new generation of students to understand and apply AI.

Adjunct Professor

Imperial College London

I help the College to define its strategic development, research themes, academic programs, and partnerships with the industry.

Industry Board Member at Dyson School of Design Engineering

samsamia

We developed a SaaS solution to increase fashion e-commerce revenue through visual search. Our technology allowed users to take a picture of a garment they liked, and find the most similar garment within the customer's catalog. I led the engineering and sales team. Among our customers, we had one of the biggest e-commerce in the UK and one of the biggest media companies in Spain.

CEO & Co-Founder

King's College London

Research topics: Learning from Demonstration, Human-Robot Interaction, Markov Chains, and 
Non-linear control.

External Researcher

Universidad Carlos III de Madrid

Research in Machine Learning for robotics: Learning from Demonstration, Computer Vision, kinematic and dynamic control of humanoid robots, and non-linear control. See my publications: ➡️➡️ https://github.com/miguelgfierro/publications

Professor at several Engineering degrees teaching Control Engineering, Industrial Automation, and Industrial Robotics.

Research Assistant at Robotics Lab

Development of algorithms to predict the energy price fluctuation in Spanish electrical market focusing in wind turbines.

Scholarship Researcher in Electric Department

Asociación de Robótica de la Universidad Carlos III

A non-profit student organization at Universidad Carlos III de Madrid. Development of R&D robotics projects including mobile robots, aerial robots (Unmanned Aerial Vehicles UAVs), 3D printers, and small humanoid robots.

President & Co-Founder

I lead the Personalization team at Microsoft working on Reco Systems, NLP, Computer Vision, and other ML solutions.

In addition, I help people understand and apply AI. Whether you want to switch your career to Data Science, land a job in a big tech company, grow your Data Science career, or apply AI to your business, I can help you.

Subscribe to my list ➡️➡️ https://bit.ly/emaillistmiguel ⬅️⬅️

____________________________
Blog: https://bit.ly/miguelgfierro
GitHub: https://github.com/miguelgfierro
Microsoft Recommenders: https://github.com/microsoft/recommenders
Publications and conferences: https://github.com/miguelgfierro/publications

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Miguel Fierro

I’ve finished the book: Building AI Agents with LLMs, RAG, and Knowledge Graphs. Really strong book to get a holistic view on how to build agents.

Here are some ideas I found interesting:

𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠:
Problem: If there are too many documents or they are too long, RAG would struggle with precision (chunks are misaligned) or recall (find all relevant chunks).
Solution: We generate a summary of each document and index it with the chunks. At query time, first search over the summaries, and then retrieve the relevant chunks.

𝐒𝐞𝐥𝐟-𝐑𝐀𝐆:
Problem: The query is too complex.
Solution: Use a multi-step process to critique and reflect on the output.

𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆:
Problem: Traditional RAG fails to capture relationships and struggles with redundant information.
Solution: Use a Knowledge Graph, composed as tripplets of entity-relation-entity, as a controlled context to increase relevance.

𝐆𝐫𝐚𝐩𝐡 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 (𝐆𝐨𝐓):
Problem: The query is too complex.
Solution: Use multiple reasoning steps in graph form to explore alternative answers and verify the final output.

𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐗𝐑𝐋):
Problem: It’s not easy to understand the agent's behavior in an RL setting.
Solution: Use an LLM as a policy interpreter to explain the agent’s behavior.

Link: https://lnkd.in/dNybQPcj

Wavicle Data Solutions

Lead Data Engineer

LinkedIn

LinkedIn Learning Instructor

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2024 
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

73% of data pipeline failures happen because of dependency issues, not bad code.
Cron jobs don't care if yesterday's data never arrived. They'll happily run on empty tables and produce garbage.

Airflow? It checks, retries, alerts, and refuses to cascade your failure downstream.

→ Beyond scheduling Airflow gives an important feature of 𝗗𝗔𝗚𝘀(𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀), that helps to monitor, retry and gives flexibility to organize pipelines with minimum/less code.

🤔 Data Engineers often misinterpret Airflow just as an orchestration tool.

Apache Airflow is an open-source platform designed to programmatically author, schedule, retry and monitor workflows. 

🎼Think of it as the "conductor of an orchestra", where each musician (your data tasks, like data extraction, transformation, loading, or model training) knows when to play, what to play, and what happens if they make a mistake.

Most data engineers automate the wrong things at the wrong time.

Here's the framework I use after 8 years of building production systems:
✅ AUTOMATE WHEN: 
→ Task runs daily/weekly 
→ Human errors cause outages 
→ Work blocks other priorities
→ Team growth = more manual work
Examples: Reports, schema checks, alerts

❌ DON'T AUTOMATE WHEN: 
→ Task happens quarterly 
→ Requirements change weekly 
→ Process isn't understood yet 
→ Manual steps reveal insights

My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it.
Automate what matters, when it matters—not everything!

Airflow isn’t just about running tasks.

🕑 It’s about building trustworthy, scalable, and observable pipelines that run reliably at 2am — even when you’re not watching.

Airflow gives structure to your chaos.
DAGs give clarity to your pipelines. 
That’s why DEs love them ❤️

Real Example: 
Instead of: 
❌ Cron jobs that fail silently 
❌ Manual dependency management
❌ No visibility into failures
You get: 
✅ Visual workflow monitoring 
✅ Automatic failure notifications
✅ Smart task scheduling 
✅ Easy debugging & restarting

Here's an amazing Airflow cheatsheet with some fundamentals concepts and interview questions to prepare for data engineers. 👇

Carousel Credits -  Futurense.com

Willing to learn more, here's the link to Astronomer free learning path - https://lnkd.in/g8-EWBda

Also, level up with Marc Lamberti's Udemy course on Airflow 3 - https://lnkd.in/gwArwaME

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The Battle

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