Was macht ein Ingenieur für künstliche Intelligenz?

An AI Engineer can be thought of as a fusion of two separate responsibilities in the AI Value Chain - the AI Scientist (more of an AI counterpart of the typical Data Scientist) and the ML Engineer. That means having the core algorithmic, mathematical & programming knowledge to understand and implement algorithms in a POC AI notebook, but also having the deployment skills to take that AI notebook to production and complete the DevOps cycle of Model Monitoring and Continuous Improvement. This is how the AI Engineer is the SPOC for the entire lifecycle of an AI project. It's a demanding role that needs both depth and breadth, and is hence an extremely valuable role in any AI / Data Science team.

AI engineers design and implement machine learning systems to address business problems. They collect and preprocess data, engineer those features, and select appropriate models. After training and optimizing models, they deploy them into production environments. Ongoing monitoring, maintenance, and ethical considerations are crucial aspects of their responsibilities.

AI engineers navigate business challenges by breaking them down, analyzing data, and designing impactful solutions. They prioritize clarity over complexity, advocating for efficient approaches rather than sophisticated neural networks for everything. A key aspect of AI engineers is the ability to communicate and convince their top management to set right expectations for deployment. AI can be an art and science but not magic.

An AI / ML engineer builds AI models using machine learning algorithms, which can be used to make business decisions, recommendations or create other content (eg by predicting the next most likely word). AI / ML engineers have a sound understanding of programming, software engineering and data science. As an AI / ML engineer, you need to perform certain tasks, such as develop, test, and deploy AI models through programming algorithms like random forest, logistic regression, linear regression, and so on.

The role of an AI engineer encompasses the comprehensive management of the AI project lifecycle, spanning from defining objectives to the deployment and integration of AI models. The nature of the project may determine whether the AI engineer works independently or collaborates within a team, involving cooperation with other engineers, data scientists, domain experts, and stakeholders. Staying abreast of the latest trends and advancements in AI is crucial for success, as is the identification and application of best practices and tools relevant to the project. In practice, AI engineers engage in diverse tasks, including the development and testing of various AI models utilizing frameworks like TensorFlow or PyTorch.

To succeed as an AI engineer, a blend of technical skills, industry knowledge, and soft skills is essential. Technical skills include proficiency in programming languages like Python, Java, and C++, along with a strong foundation in data structures, algorithms, machine learning, and software development. Understanding the specific industry, such as healthcare or finance, is crucial for creating relevant AI solutions. Moreover, soft skills like effective communication, teamwork, and problem-solving are vital for collaboration and continuous learning. This combination of diverse skills enables AI engineers to develop innovative solutions and adapt to the rapidly evolving AI landscape.

With the new generation of GenAI, AI engineers should be a good teacher: be thoughtful about what you teach, be creative about how to share your knowledge, and finally be patient with your students.

Becoming an AI engineer requires a diverse set of skills and knowledge. Key technical skills encompass proficiency in programming languages such as Python, Java, and C++; a solid grasp of data structures and algorithms; a foundation in mathematics and statistics; expertise in machine learning and deep learning techniques; and proficiency in data engineering and analysis. Additionally, skills in software engineering and development are crucial for building and deploying AI solutions effectively. Understanding the specific domain or industry in which you operate is equally important.

An AI engineer needs a solid math foundation. The programming aspect isn't the most challenging because AI engineers primarily deal with matrices. The key skill is obtaining a comprehensive and diverse dataset that covers all potential outcomes. Optimizing runtimes is the part where computer science can offer a real advantage!

An artificial intelligence engineer generally requires programming language skills such as Python especially its AI frameworks such as TensorFlow and PyTorch, understanding of concepts such as linear algebra, calculus, probability and statistics, to be able to tackle complex problems and find innovative solutions is essential in the field of AI, able to effectively communicate their ideas and results to both technical colleagues and non-technical stakeholders.

There are few real challenges faced by AI Engineers: Unrealistic top management expectations on timelines and performance. Ability to design approaches ensuring no bias or hatred arises as outcome. The amount of time spent on extracting the valuable data is much higher than design, implementation of the actual algorithm. Identifying the right metric or KPIs to measure your approaches outcome is very important.

Ethics and social responsibility. As an AI engineer, you may have to deal with ethical and social issues that arise from the development and deployment of your AI solutions, such as privacy, security, bias, discrimination, accountability, transparency, explainability, and human dignity. You may also have to consider the potential risks, harms, and impacts of your AI solutions on individuals, groups, and society, and ensure that your AI solutions are aligned with the values, norms, and laws of the relevant contexts. Therefore, you need to have a good understanding of the ethical and social implications of your AI solutions, as well as the ability to apply ethical principles and frameworks to guide your decision-making and actions.

Open source contributing is one way of getting a handful of opportunities from large companies. AI Engineers are highly requested in automotive and tech companies, but we are seeing an increase in every field to implement large language models in order to improve human ressources department.

O maior desafio é se manter atualizado. Ser engenheiro de IA hoje é ligar o computador e ficar 1/3 (ou mais) do seu dia vendo o que mudou de ontem pra hoje. Além disso, é um cargo que n?o é t?o bem definido quando um engenheiro de front-end por exemplo, algumas empresas mal tem ideia do que fazer com um eng. de IA. Já as oportunidades est?o nas alturas, entre meio de 23 até o final de 24 devem surfar na crista da onda.

AI engineers face challenges such as ethical considerations, ensuring fairness and transparency in models, and addressing biases. Data quality and availability can also pose obstacles, along with the need for robust security measures to protect AI systems. Keeping up with the rapid advancements in AI technology and handling the complexity of deploying models into real-world scenarios are ongoing challenges. However, these challenges present opportunities for AI engineers to contribute to ethical AI practices, develop innovative solutions, and enhance the reliability and accessibility of AI technologies.

AI engineers face challenges in data quality, interpretability, ethical considerations, computational resource demands, staying updated with evolving technologies, and integrating AI solutions into existing systems. These challenges highlight the complexity of developing, deploying, and maintaining AI systems while addressing ethical and practical concerns in a rapidly evolving field.

As an AI engineer, you will be responsible for researching and developing artificial intelligence systems using various algorithms and techniques such as machine learning, deep learning, and computer vision. You will collect and analyze data, build and train models, evaluate and validate their performance, deploy them into production environments, and monitor their maintenance and updates. Collaboration with cross-functional teams is essential to address specific challenges and incorporate AI capabilities into existing products or services. Overall, AI engineers play a critical role in advancing AI technologies to solve complex problems and improve various industries and society.

Aqui temos muito do que o mercado já é, como trabalhar em equipe, resiliência, cultura da empresa e etc. O que realmente deve ser considerado é: como entregar valor com algo novo de forma disruptiva? A gra?a de navegar em novos mares é a descoberta de um novo mundo, tentar fazer o de sempre, só que agora com IA pode parecer legal, mas n?o é usar todo seu potencial.