The Rise of the AI Engineer

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The Rise of the AI Engineer

In this issue, I dig into the fresh and exciting world of AI Engineering. I'll chat about what an AI Engineer does and lay out what it takes for you to become one yourself.

Today, I’ll cover the following:

• What is an AI Engineer?

• Responsibilities of AI Engineers

• Why AI Engineer is the role of the future?

• AI Engineers and Their Relationship with Software Development

• Salary and Job Outlook for AI Engineers

• How to Become an AI Engineer?

Let’s dive in 🤿

What is an AI Engineer?

AI Engineering focuses on developing 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 capable of tweaking and hosting models and also adept at productizing AI, dealing with the challenges in evaluating and applying 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 with chains, retrieval, vector search tools, and the consumption and application of the daily influx of new papers, models, and techniques. This role needs to keep up with all the latest advancements.

This role has emerged and is rapidly gaining recognition among startups and large corporations. At IBM, we are going all in with the AI Engineering profession. I hired in my team at IBM more than 300+ AI Engineers worldwide last year, from new graduates to very senior engineers and managers.

Interestingly, the backgrounds of these AI Engineers are diverse. Today, you can find them at leading companies like Microsoft, IBM, Google and startups like Figma. These professionals are proving that a Ph.D. isn't necessary to ship AI products. 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.

Some argue that the future AI engineer will need a breadth of knowledge far beyond what can be acquired in a few days. This role would require a deep understanding of various AI models, their limitations, and the ethical implications of their use. It's not just about calling an API; it's about architecting solutions that leverage AI effectively and responsibly.

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 valuable reference for the work expected to be mastered 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 box 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 Infrastructure: Develop and maintain the infrastructure for AI development and deployment, ensuring AI systems are scalable, reliable, and efficient while working with traditional cloud providers and new hosting solutions.

• Apply Prompting Strategies for Foundation Models: Develop and improve prompting strategies for Large Language Models (LLMs) using frameworks like LangChain and LlamaIndex, and execute prompt inference with integrated logging, caching, and validation.

• Data Management: Use "in-context learning" to efficiently handle large datasets with LLMs like Llama 3, preprocess and embed data by breaking it into chunks, processing with an embedding model, and storing outputs in a vector database.

• Manage Language Models and Tools: Navigate and transition between various language models for production use, understand and manage model licensing for commercial and experimental use, and ensure compliance.

• Integrate AI Models into Applications: Transform machine learning models into APIs for seamless integration with other systems, enabling smooth interaction between AI models and other software components.

• Foster Collaboration and AI Best Practices: Collaborate with cross-functional teams to promote AI adoption, share knowledge, and promote best practices in AI engineering and development.

By fulfilling these responsibilities, AI engineers contribute to successfully implementing AI solutions, enabling organizations to unlock AI's full potential.

The Hype and Reality

The term "AI engineer" has undoubtedly gained traction, partly due to the immense hype surrounding AI. Companies are eager to jump on the AI bandwagon, often without understanding how to leverage the technology effectively. This eagerness can lead to a proliferation of AI roles that might not require the depth of expertise traditionally associated with engineering titles.

Critics argue that this trend is reminiscent of the blockchain hype, where every tech company felt compelled to incorporate blockchain into their products, regardless of its utility. The result was a mix of genuine innovation and many overblown promises. The AI industry is at risk of a similar pattern, where the demand for AI expertise outstrips the actual supply of knowledgeable professionals, leading to inflated titles and, in some cases, inflated salaries.

Salary and Job Outlook for AI Engineers

The field of Artificial Intelligence (AI) offers lucrative salaries and a promising job outlook due to high demand and a talent shortage.

Salary

• Entry-Level: $80,000 to $120,000 annually.

• Mid-Level: $120,000 to $160,000 annually.

• Senior Level: $160,000 to $200,000+ annually.

• Top Positions: Over $300,000, with total compensation often exceeding $500,000.

Job Outlook

• High Demand: AI is essential in healthcare, finance, automotive, and more.

• Technological Advancements: Continuous innovation fuels demand for skilled professionals.

• Talent Shortage: A significant shortage ensures strong job security and career growth.

• Global Expansion: Increasing opportunities worldwide, not just in tech hubs.

AI engineering offers incredible salaries, diverse opportunities, and a bright future, making it an attractive and secure career path.

AI Engineers and Their Relationship with Application Developers

The role of AI engineers and software developers is increasingly converging as AI becomes integral to modern applications. Today, all app developers are effectively becoming AI developers as AI technology becomes more accessible and easier to use. Key factors driving this transformation include:

• Integrating AI Models: Developers now regularly incorporate AI models into applications, enhancing functionality and user experience. Foundation models accessible via APIs simplify this process.

• Utilizing AI Tools: Familiarity with AI frameworks like LangChain and LlamaIndex is becoming a standard skill for software developers. These tools offer user-friendly interfaces and extensive documentation, making implementing AI without ML expertise.

• AI Building Blocks: The rise of AI building blocks allows developers to integrate sophisticated AI-driven applications with minimal effort.

• Collaborating Across Disciplines: Developers work in cross-functional teams with AI specialists to ensure seamless integration and performance. This collaboration fosters innovation and ensures that AI features are effectively incorporated into software projects.

• Driving Innovation: From predictive analytics to personalized recommendations, developers leverage AI to create smarter, more intuitive applications across industries. The accessibility of AI tools empowers developers to experiment and innovate, pushing the boundaries of what applications can achieve.

As AI continues to evolve, the distinction between traditional app developers and AI developers is blurring. The growing ease of use and availability of AI technologies mean that all app developers can now harness the power of AI to build more intelligent and adaptive applications.

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 and 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 must have strong soft skills like 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: Some many online courses and tutorials 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: These are great ways 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. I encourage you to pursue this path if you're interested in AI.

Difference between an AI Engineer and a Data Scientist

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

Data scientists collect, clean, analyze, and interpret data. They use their statistics, machine learning, and programming skills to extract insights from data that can help businesses make better decisions. Data scientists also build and deploy machine learning models but typically don't focus on the underlying infrastructure.

AI engineers are responsible for building and deploying AI systems. They deeply understand 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. Because of its systems-heavy workloads, LLM training is becoming sufficiently different from ML and splitting off into a new kind of role, focused on very large-scale training of transformers on supercomputers.

Conclusion

AI engineers are at the forefront of technological evolution, revolutionizing our interactions with AI. They develop and deploy AI systems and adapt to new trends, like pre-trained AI models, which have democratized AI development. AI engineers are set to play a crucial role as we stand on the brink of even more extensive integration of LLMs. 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 essential today and increasingly important as we journey into the AI-driven future.

My prediction:

Enjoy the weekend, folks!

Armand

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