The AI Engineer role

AI is experiencing an unprecedented rise, and the professionals building those systems are evolving from a niche role into a dynamic and emerging field - the AI Engineer.

This transformation is fueled by advancements in AI capabilities and the increasing availability of Foundation Models, which have shifted the accessibility of AI tasks. What once took a research team years to complete is now achievable in an afternoon with the right API documentation.

In today's issue, I will describe:

• What is an AI Engineer?
• Responsibilities of AI Engineers
• Why AI Engineer is the role of the future?
• Salary and Job Outlook for AI Engineers
• How to Become an AI Engineer?

Let's dive into it 🤿

What is an AI Engineer?

The focus of AI Engineering lies in the development of tools, systems, and processes that enable the practical application of AI. This encompasses various areas where machines replicate human functions, including problem-solving and learning.

This new breed of AI Engineers is not only capable of tweaking and hosting models but also adept at productizing AI, dealing with the challenges in the evaluation and application of a wide array of models, from the largest Large Language Models to open-source task-specific models available in the Huggingface Hub.

AI Engineers are comfortable dealing with chains, retrieval, and vector search tools, along with the consumption and application of the daily influx of new papers, models, and techniques. AI Engineering is a role that needs to keep up with all the latest advancements.

This role has emerged and is rapidly gaining recognition in startups and large corporations alike. At IBM we are going all in with the AI Engineering profession. We have right now more than 300+ openings worldwide and I'm actively hiring at all levels, from new graduates to very senior engineers and managers.

I believe the AI Engineer will be the highest-demand engineering job of the decade.

Interestingly, the background of these AI Engineers is diverse. Today you can find them at leading companies like Microsoft, IBM, and Google, as well as startups like Figma. These professionals are proving that a Ph.D. isn't necessary to ship AI products. In fact, they are transforming AI advancements into practical applications used by millions, virtually overnight.

In short, the AI Engineer represents a generational shift in software engineering, playing an essential role in turning the promise of AI into tangible value.

Responsibilities of AI Engineers

In organizations leveraging AI, AI engineers play a vital role by spearheading AI strategies and identifying areas where AI can provide solutions. They are responsible for establishing robust AI development and production infrastructure and overseeing its implementation.

In the publication Emerging Architectures for LLM Applications form a16z, they shared the most common systems, tools, and design patterns to build sophisticated and new-generation AI solutions. This stack is rapidly evolving, but it is a useful reference for the work that is expected to master as part of the AI Engineering profession.

If you want to be an AI Engineer, this App Stack is a good starting point: learn about each of the boxes of this diagram and how they all work together.

Here are key tasks and responsibilities typically carried out by AI engineers:

Design and manage AI development and production infrastructure:

• Develop and maintain the infrastructure required for AI development and deployment.
• Ensure scalability, reliability, and efficiency of AI systems. Effectively work with both traditional cloud providers and emerging hosting solutions.

Apply Prompting Strategies for Foundation Models:

• Develop and refine advanced prompting strategies for Large Language Models (LLMs), leveraging frameworks like LangChain and LlamaIndex to perform tasks such as prompt chaining and memory maintenance across LLM calls.
• Execute prompt inference by submitting the compiled prompts to the pre-trained LLM, ensuring the integration of operational systems like logging, caching, and validation.

Data Management:

• Apply "In-context learning" design patterns to efficiently leverage Large Language Models (LLMs), such as GPT-4, especially when handling large datasets.
• Execute data preprocessing and embedding by breaking down data into manageable chunks, processing them through an embedding model, and storing the outputs in a specialized vector database.

Management of Language Models and Operational Tools:

• Navigate various language models, transitioning from models like GPT-4 or GPT-4-32k to more scalable options for production, maintaining pace with the evolving landscape of language models.
• Understand and manage model licensing requirements for both commercial and experimental use, ensuring compliance with all applicable regulations and agreements while maximizing the utilization of AI models in product development.

Integrate AI models into applications:

• Transform machine learning models into accessible APIs that can be seamlessly integrated into other applications or systems.
• Enable smooth interaction between AI models and other software components.

Foster collaboration and promote AI best practices:

• Collaborate with cross-functional teams to facilitate AI adoption across the organization.
• Share knowledge and promote best practices in AI engineering and development.

By fulfilling these responsibilities, AI engineers contribute to the successful implementation of AI solutions, enabling organizations to unlock the full potential of artificial intelligence.

Salary and Job Outlook for AI Engineers

AI engineering is a specialized field with promising job prospects and competitive salaries.

According to Glassdoor, the median base salary for AI engineers in the United States is $101,991. Additionally, the US Bureau of Labor Statistics categorizes AI engineers under the occupation of Computer and Information Research Scientists, with a median salary of $131,490.

The job outlook for AI engineers is highly favorable, with a projected job growth of 22 percent between 2020 and 2030. This growth rate is significantly faster than the average for all occupations, which stands at 8 percent.

AI engineers predominantly find employment in technology companies such as Google, IBM, and Meta, where they contribute to improving products, software, operations, and delivery processes. Moreover, there is an increasing demand for AI engineers in government agencies and research facilities dedicated to enhancing public services.

These positive salary trends and high demand for AI engineering expertise indicate a bright career outlook for professionals in this field.

How to Become an AI Engineer

To become an AI engineer, you need to learn a variety of skills, including:

1. Programming & Mathematics: Master Python and its AI libraries along with core concepts in statistics, linear algebra, and calculus.
2. Machine Learning & Deep Learning: Understand the basics of machine learning, deep learning, and their subfields like NLP and computer vision.
3. Software Engineering & Cloud Computing: Learn system design, software development practices, and get comfortable with cloud platforms like AWS, GCP, or Azure.

Here's a list of courses that I highly recommend to get all the skills you need to succeed in this highly technical role:

• Coursera: IBM AI Engineering Professional Certificate
• DeepLearning.ai: ChatGPT Prompt Engineering for Developers
• DeepLearning.ai: LangChain for LLM Application Development
• DeepLearning.ai: Building Systems with the ChatGPT API
• DeepLearning.ai: LangChain: Chat with Your Data
• DeepLearning.ai: How Diffusion Models Work
• Linkedin: Prompt Engineering: How to Talk to the AIs
• Activeloop: LangChain & Vector Databases in Production
• Coursera: Build a Data Science Web App with Streamlit and Python

In addition to these technical skills, AI engineers also need to have strong soft skills, such as communication, problem-solving, and teamwork.

Here are some steps you can take to become an AI engineer:

1. Get a degree in a related field. A bachelor's degree in computer science, data science, or a related field is a good foundation for a career in AI engineering.
2. Take online courses and tutorials. There are many online courses and tutorials that can teach you the skills you need to become an AI engineer. Start with my list above.
3. Get involved in open source projects. Working on open source projects is a great way to gain experience in AI engineering.
4. Attend conferences and meetups. Attending conferences and meetups is a great way to network with other AI engineers and learn about new trends in the field.
5. Build your portfolio. Create a portfolio of your AI projects to showcase your skills to potential employers.

Becoming an AI engineer is a challenging but rewarding career. If you're interested in AI, I encourage you to pursue this path.

Difference between an AI Engineer and a Data Scientist

Data scientists and AI engineers are both in high demand in the tech industry, but they have different roles and responsibilities.

Data scientists are responsible for collecting, cleaning, analyzing, and interpreting data. They use their skills in statistics, machine learning, and programming to extract insights from data that can help businesses make better decisions. Data scientists also build and deploy machine learning models, but they typically don't focus on the underlying infrastructure.

AI engineers are responsible for building and deploying AI systems. They have a deep understanding of machine learning algorithms and how to implement them in production. AI engineers also work on the underlying infrastructure that powers AI systems, such as data pipelines, cloud computing platforms, and machine learning frameworks.

LLMs created a whole new layer of abstraction and profession. LLMs training is becoming sufficiently different from ML because of its systems-heavy workloads and is also splitting off into a new kind of role, focused on very large-scale training of transformers on supercomputers.

AI Engineers can be quite successful in this role without ever training anything.

Conclusion

AI engineers are at the forefront of technological evolution, revolutionizing how we interact with AI. They not only develop and deploy AI systems, but they also adapt to new trends, like pre-trained AI models, which have democratized AI development. As we stand on the brink of even more extensive integration of Large Language Models (LLMs), AI engineers are set to play a crucial role. Their work will shape the trajectory of AI development, crafting strategies, patterns, and architectures that define the future of the digital world. Their role is not only essential today but increasingly important as we journey further into the AI-driven future.

My personal prediction:

In numbers, there are probably going to be significantly more AI Engineers than there are data scientists.