Becoming an AI Engineer : Practical Guide for Software Engineers

9 min readJun 9, 2024

Madhusudhan Konda

Gentle Introduction to AI/ML

Artificial Intelligence (AI) has moved from the realm of science fiction into everyday reality, transforming industries and reshaping the future of work.

For software engineers, the transition to AI engineering offers a unique opportunity to be at the forefront of this technological revolution.

As a software engineer, you already possess a strong foundation in problem-solving, coding, and systems architecture. These skills are critical for AI — the complex algorithms, data processing, and innovative solutions — all are key ingredients to becoming an AI engineer.

However, moving from software development to AI engineering involves learning new concepts, mastering specialised tools, and developing a keen understanding of machine learning and data science.

The demand for AI expertise is skyrocketing across various sectors including healthcare, finance, automotive, and more. Companies are leveraging AI to enhance decision-making, automate processes, and deliver smarter products and services.

By becoming an AI engineer, you not only expand your skill set but also position yourself for exciting career opportunities and the chance to work on groundbreaking technologies that could change the world.

What This Guide Offers

This guide is designed to provide a practical, step-by-step pathway for software engineers to become AI engineers.

I will explore the fundamental concepts of AI through practical hands-on examples, delve into machine learning and deep learning. I offer hands-on projects to apply your knowledge. Along the way, you’ll gain insights into the necessary mathematical foundations, the most useful programming languages, and the best resources to stay current in this rapidly evolving field.

Whether you’re looking to transition into a new role, enhance your current skills, or simply explore the fascinating world of AI — this guide will serve as your roadmap.

So, let’s embark on this journey together and unlock the incredible possibilities that AI has to offer.

Understanding AI, Machine Learning, and Generative AI

Field of study that gives computers the ability to learn without being explicitly programmed — Arthur Samuel (1959)

As a software engineer stepping into the realm of artificial intelligence (AI), it’s essential to grasp the key concepts that define this transformative field.

AI encompasses a broad spectrum of technologies, with machine learning and generative AI being among the most significant.

Artificial Intelligence: Helicopter View

Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include logical reasoning, learning, problem-solving, understanding natural language, perception and others.

AI systems are expected to analyse complex (huge sets) data and recognise patterns in that data. It then helps them make decisions with minimal human intervention.

What does it mean in practical terms?

AI is about building intelligent machines that perceive their environment and take actions to achieve specific goals.

For software engineers - this means leveraging algorithms and computational techniques to create applications that can automate processes, provide insights, and enhance user experiences. AI is not just about making machines “intelligent” or “smarter” but about enhancing human capabilities.

AI Examples

Here are some practical examples where AI can be used (a long list exists — but here are a small sample):

AI systems are used to analyse medical images such as X-rays, MRIs, and CT scans to identify abnormalities like tumours, fractures, or other pathologies. For instance, a neural network can be trained to detect cancerous cells in mammograms.
AI algorithms can recognise and verify individuals by analyzing facial features. This technology is used in security systems for access control and in social media for tagging photos.
AI is used to analyse text data from social media, reviews, or surveys to determine the sentiments — positive, negative, or neutral expressions - helping companies understand customer opinions and improve their products or services.
AI algorithms analyse historical financial data to predict future trends — this helps businesses and investors make informed decisions about market trends and economic conditions.
AI is employed in autonomous vehicles to interpret sensory data from cameras to navigate roads, detect obstacles, and make driving decisions without human intervention.
AI is used to control drones for tasks such as surveillance, agricultural monitoring, and package delivery — assisting in autonomous navigation
AI algorithms analyse user behaviour and preferences to recommend products on e-commerce platforms like Amazon — to help increase sales and improve user satisfaction.
Streaming services like Netflix and Spotify use AI to suggest movies, shows, or music based on a user’s past viewing or listening history
AI systems analyse transaction patterns to detect and prevent fraudulent activities in real-time — to assist in reducing the risk of financial losses.
AI is used to identify and respond to security threats by analysing network traffic and detecting unusual patterns that may indicate a cyber attack.
AI techniques uncover hidden patterns, correlations, and insights — this leads to data driven drive decision-making in fields like healthcare, finance, and marketing.
AI analyses equipment data in manufacturing and aviation industries to predict maintenance needs and prevent breakdowns — thus reducing waste, downtime and costs.
AI powered virtual assistants (Siri, Alexa, and Google Home) understand and respond to voice commands, control smart home devices, and provide information.
AI chatbots are used in customer service to answer queries, provide support, and assist with transactions — thus improving customer engagement and satisfaction.

Machine Learning

Machine Learning (ML) is a subset of AI — that helps computers make forecasting and predictions or decisions based on data. Instead of being explicitly programmed for every task, ML models are trained on large datasets. They use statistical methods to identify patterns and relationships.

Unlike traditional software development, where you write specific instructions for the computer to follow, ML involves feeding large amounts of data into algorithms that learn to make decisions or predictions based on that data.

Common ML techniques include regression, classification, clustering, and reinforcement learning. As you delve deeper into ML, you’ll encounter these algorithms and learn how to apply them to solve real-world problems.

For a software engineer, mastering ML opens up opportunities to build intelligent systems that can learn and adapt over time.

Generative AI

Generative AI is a cutting-edge branch of AI that focuses on generating new content — such as text, images, music, and even code — all by learning from existing data.

Generative AI can be used to create synthetic data for training other AI models as well as design new products

As a software engineer, you can leverage generative AI to development and design processes, create testing data, enhance code and more.

Leveraging APIs, Tools, and Frameworks in AI Engineering

As you progress in your journey to become an AI engineer, understanding and utilising APIs (Application Programming Interfaces), along with specialised tools and frameworks is important.

This section introduces you to some of the most impactful APIs, tools, and frameworks that are shaping the AI landscape today. We will look at them in detail using the hands-on code shortly.

APIs are interfaces that allow different software applications to communicate with each other. In the context of AI - APIs provide access to powerful pre-trained models and services. They enable us to integrate advanced AI functionalities into our projects with ease.

Here are some noteworthy APIs and tools and fraemworks that are transforming how AI is implemented across various applications. While I touch them here gently, we will be exploring them in detail in coming days.

1.1. OpenAI API

One name stands out when we talk about Generative AI — that’s OpenAI — the creator of ChatGPT.

Here’s the UI face of OpenAI — the ChatGPT and here’s the the backend OpenAI API platform. We will be needing to signup for the playground by creating an account on OpenAI.

Don’t get confused with ChatGPT and API Playground. The ChatGPT is the UI — the frontend while the backend is the API Playground.

You need to fund your OpenAI’s developer account at least with a $5 so you can test the access!

OpenAI is at the forefront of AI research and development, offering APIs that allow developers to access some of the most advanced AI models available today. The OpenAI API provides a gateway to powerful language models like GPT-3, GPT-4 and GPT-4o enabling a wide range of applications from natural language processing to content generation and beyond.

With OpenAI’s models, you can build applications that understand and generate human-like text, enabling functionalities like chatbots, language translation, and more.

You can also fine-tune models on specific datasets on APIs, providing the flexibility to tailor the AI to meet the unique needs of your projects.

By integrating the OpenAI API into our applications, we can harness the power of advanced AI without needing extensive expertise in machine learning.

Example Applications:

Building intelligent virtual assistants that can handle customer queries with human-like responses.
Creating content generation tools for automating writing tasks, such as generating reports, articles, and summaries.

While ChatGPT is the UI interface, as an engineer, we expect the APIs to work with. OpenAI provides a set of comprehensive APIs for developers to integrate with the Large Language Models.

1.2 LangChain framework

While it is not needed to embrace another framework, should you expect to work with multiple models (like not just OpenAI’s GPT models, but probably Mistral, Meta’s Llama 3 etc), you would soon realise the importance of working with an abstraction framework like LangChain.

Visit LangChain and get familiarise with their APIs. Checkout their docs and APIs for Python (and JavaScript)

LangChain is an emerging framework that simplifies the process of developing applications with large language models (LLMs). It provides a suite of tools and components designed to help you manage and manipulate language models effectively, enabling rapid development and deployment of AI-powered applications.

LangChain offers a modular approach to building applications by allowing you to create pipelines that link various components: such as large language models, data preprocessors, and output handlers.

The framework supports integration with various language models, not just from OpenAI, but also from other providers (like Azure, AWS, Cohere..) — thus giving you flexibility in choosing the best model for your application.

LangChain provides tools for interactive experimentation and testing, allowing us to iteratively develop and refine your language-based applications with ease.

1.3 Python Programming Language

When it comes to AI applications, Python stands out as the preferred programming language for several compelling reasons. Python boasts a rich ecosystem of libraries and frameworks that simplify various aspects of AI development:

TensorFlow: A powerful framework for building deep learning models.
PyTorch: Popular for its flexibility and ease of use in neural network building.
Scikit-learn: Provides simple and efficient tools for data mining and data analysis.
Keras: An API designed for human beings, not machines, that allows for easy and fast prototyping.

Python has a large and active community which provides extensive support, resources, and tools for AI development. This includes open-source contributions, forums, and online courses that facilitate knowledge sharing and problem-solving.

If you are a visual learner, there are a ton of great Python introductory courses on YouTube! Just pick one or two and get your Python skills up!

1.4 IDEs

We also need an Integrated Development Environment (IDE) for devolping and testing our applications.

While this may boil down to your personal choice, I suggest at least to consider two great free tools:

Visual Studio for Code: This is a free awesome tool (I’ve moved to this VS Code IDE few years back and this is my go-to toll for most everything) that you can download and install on your machine without much of a hassle.

Download VS Code from Microsoft’s official site.

Jupyter Notebook: Dubbed to be the data scientist/ML Engineer’s favourite tool, Jupyter notebook is an easy-to-learn-experiment-and-document your AI applications! You have a few options, two of which are given below:

Anaconda: Anaconda is the all-in-one solution for developing AI applications. Download it from the Anaconda official installation site for free and get it installed. Once the package is installed, you should start the Jupyter straightway.
Google Colab: Fortunately, Google provides an online Jupyter notebook for free! visit Colab and get started!

That’s pretty much the required tools — all you need is an open mind and a focussed time.

Stay tuned to develop a simple LangChain application in the next article.

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