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The Road to Becoming an
ML Engineer

A Career Journey Designed to take you from Backend to Machine Learning engineer.


Machine Learning is changing the world. From content personalization and spam filters, to self-driving cars and Virtual personal assistants or even cold fusion, ML workflows are now a first class citizen in the software industry.

How does it work

This career Journey offers you a unique opportunity to develop your Machine Learning skills and knowledge alongside your daily assignment. With a combination of hands-on (80%) and training (20%) throughout the work week.

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80% hands on

(4 days a week)

put your learning into practice.

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20% training & learning

(1 day a week)

  • Prepherals Fundamentals

  • Statistics Fundamentals & Modeling

  • Advanced Data science tools in Python

  • Machine learning

  • SQL Basics

  • Big data handling

  • Neural networks

  • Coding BP

We are offering experienced backend engineers an opportunity to get into the exciting world of Machine Learning. Will it be you?

Ready to join Tikal and continue your tech journey?

If you think you have what it takes, apply now, and maybe you’ll find yourself working alongside some of the world’s best teams!

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ML Form

Watch Q&A From the Panel discussion how to become ML Engineer

What you’ll learn 

Prepherals Fundamentals

  • AWS

  • Git

  • Linux

Statistical modeling

  • Probability & decision making

  • Distributions and Parameter estimation

  • Hypotheses testing

  • Predictive Statistics

Big data handling

  • Parallel computing

  • PySpark on python

  • Parallelising and Concurrency in Python

  • Handeling Big Data

  • Bias and Drop Analysis

Statistics Fundamentals

  • Probability basic concepts

  • Important distributions

  • LLN and CLT

  • Random variables

  • Bayesian statistics

  • Statistics Basic concepts

Machine learning

  • ML Intro

  • Regression

  • Classifiers

  • Model Selection

  • Decision Trees

  • Unsupervised learning

Neural networks

  • Back propagation

  • CNNs and RNNS

  • Common architectures

  • Training an NN model

  • Use cases

  • Custom views

Advanced Data science tools in Python

  • Jupyter

  • Scientific Python

  • Matplotlib & Seaborn

SQL Basics

  • Filtering, Sorting, and

  • Calculating Data with SQL

  • Aggregate functions

  • Subqueries and Joins

  • Modifying and Analyzing

  • Data with SQL

Coding BP

  • Best practices in big data

  • Debugging

  • Time management in data work

  • Testing

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