With the acceleration of digitalization globally, as the pandemic hit the world in 2020, companies resistant to it had to adapt quickly; this made jobs that were already critical to digital transformation even more required. In addition, as generative AI surprised the world, with the release of ChatGPT, in November 2022, the digital world further accelerated, making the jobs below a critical component for any medium to large sized organization.
Role
Description
Key Responsibilities
Industry Need
Full Stack Developers
Manage front-end, back-end, and application development to build comprehensive web tools and apps.
– Develop UI for user experience. – Manage back-end processes like database and transaction handling. – Build end-to-end applications to meet growing business demands.
Crucial for scalable web apps and tools that support businesses adapting to digital transformation, including small enterprises expanding their online presence.
Cybersecurity Experts
Safeguard systems, networks, and data against growing cyber threats and attacks, ensuring digital security.
– Implement defense mechanisms for cybersecurity. – Prevent exploits and malicious activities on systems. – Conduct risk assessments and monitor vulnerabilities.
As online traffic and cyberattacks increase, cybersecurity has become essential for protecting sensitive information and maintaining trust across all industries, especially in healthcare, finance, and government.
Blockchain Developers
Build and deploy decentralized applications (dApps) and manage distributed ledger systems for transparency and security.
– Develop smart contracts and blockchain protocols. – Manage secure, immutable databases. – Build decentralized systems using technologies like Solidity.
The rise of dApps, decentralized finance (DeFi), and enterprise blockchain applications across finance, supply chain, and real estate has spiked demand for Blockchain Developers.
Cloud Developers
Design and manage applications and platforms hosted on cloud infrastructures for scalability and efficiency.
– Develop cloud-based solutions for IaaS, PaaS, and SaaS models. – Optimize performance of applications in cloud environments. – Implement scalable and secure cloud systems.
High demand due to the proliferation of cloud computing and business models relying on subscription or pay-as-you-go services, such as SaaS platforms and hybrid cloud solutions in enterprises.
AI/ML Developers
Build and optimize AI/ML models, leveraging large datasets to enable intelligent systems and automation.
– Develop machine learning algorithms and AI-driven solutions. – Process large datasets to train AI models. – Deploy AI applications to automate business processes and enhance decision-making.
Increasing demand across industries such as autonomous systems, personalized healthcare, and customer engagement platforms, with AI driving automation and insights at scale.
DevOps & DevSecOps Engineers
Streamline software development and operations, integrating security measures to enable continuous and secure deployment.
– Implement CI/CD pipelines for automated testing and deployment. – Incorporate security practices into DevOps processes. – Monitor and optimize infrastructure performance.
Essential for enabling agile and secure development cycles in organizations adopting DevOps culture, particularly for scalable enterprise solutions and cloud-native applications.
Business Engineers
Bridge technology and business strategies, devising solutions to integrate technology into complex organizational models.
– Analyze business requirements and technical challenges. – Design technology solutions to meet organizational goals. – Act as a liaison between commercial and technical teams.
Increasing complexity in digital-first organizations demands professionals who can align technological advancements with strategic business goals, fostering innovation in industries like finance and e-commerce.
Business Designers
Craft and validate sustainable business models that integrate technology and enhance value propositions.
– Design business models tailored to market needs. – Test and validate assumptions to ensure model feasibility. – Align technology-driven innovations with organizational strategies.
Organizations transitioning to digital-first or hybrid business models require Business Designers to create competitive and sustainable value propositions in a rapidly changing marketplace.
Data Scientists
Analyze, clean, and prepare data to develop insights and predictive models essential for AI-driven decisions.
– Prepare datasets for machine learning models. – Design predictive algorithms and statistical models. – Interpret complex data to provide actionable insights.
Data Scientists drive insights for sectors such as healthcare, retail, and finance by extracting value from structured and unstructured data, serving as the backbone for AI and analytics initiatives.
Product Managers with Technical Expertise
Coordinate the development and deployment of AI-powered products, ensuring technical feasibility and alignment with business objectives.
– Define AI product requirements and features. – Oversee cross-functional teams in product development. – Monitor and iterate on product performance based on feedback and metrics.
AI-driven products require technical-savvy Product Managers to manage teams and align product features with customer expectations in industries like autonomous systems, enterprise tools, and personalized services.
Mobile App Developers
Build and optimize mobile applications integrating AI capabilities, making them efficient, user-friendly, and responsive.
– Develop AI-enhanced apps with features like voice recognition and image processing. – Optimize AI algorithms for mobile use. – Enhance app performance based on user feedback.
AI-powered mobile applications in healthcare, education, and entertainment require skilled developers to ensure seamless AI integration and user engagement.
UX/UI Designers
Design intuitive, user-friendly interfaces for AI-driven systems, making them accessible to end-users while enhancing trust and usability.
– Create interfaces for AI systems like dashboards and chatbots. – Conduct usability testing for AI-powered tools. – Simplify complex AI analytics and outputs for user comprehension.
Critical for ensuring the accessibility and usability of AI systems in applications such as customer service platforms, diagnostic tools, and investment advisory systems.
Edge Computing Developers
Design decentralized systems that process data closer to the source, reducing latency for real-time AI applications in IoT and other fields.
– Build systems for local data processing on edge devices. – Implement real-time AI solutions in IoT applications. – Optimize data flow between edge and centralized systems.
Essential for latency-sensitive AI applications such as autonomous vehicles, predictive maintenance in manufacturing, and real-time healthcare monitoring systems.
With the web, which is further scaling to penetrate most of the world’s population and to get online also small businesses.
The necessity for developers that can manage multiple aspects, that go from the UI, back-end, and applications side can be critical to building tools successfully and web apps that help businesses grow.
There are three segments of web development and design. One is dealing with the user interface or what the customer sees. Front End development is responsible for the crucial elements that make up the page’s presentation. The next is Back End, which handles the processes involved in the web page. It deals with information validation, database management, as well as transactions. As businesses continue to grow, the third segment emerged to accommodate their increasing needs and lucrative goals. Building applications from end to end is what makes a full-stack developer. It is a more versatile role considered the Jack of All Trades.
The increased traffic on the web has also resulted in more and more attacks, and cybersecurity has become increasingly important. In that respect, cybersecurity experts’ demand is more than ever.
Cybersecurity refers to the practice of implementing defense programs against cyber threats, exploits, and attacks. It aims to secure computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. Simply put, cybersecurity serves as the security unit of the entire cyberspace.
With the Blockchain finding many interesting applications in several industries, developers able to build apps on top of the various Blockchain protocols spurred in the last decade are highly in demand.
With the increase of decentralized applications, this request has exploded.
Also referred to as the Distributed Ledger Technology (DLT), blockchain is the digital asset of organizations that cannot be modified. It can be transparent for several people through decentralization and cryptographic hashing. The blockchain is in a database that gathers the information stored on a system. Databases are designed in a table format for users to quickly retrieve a block or filter specific data. This technology has the potential to disintermediate trust and decentralize any system.
The new software paradigm, hosted on the cloud rather than on the client-server and on-premise, has determined an explosion of apps, platforms, and infrastructures built on top of the cloud.
Thus, making developers for the cloud in high demand.
Cloud business models are all built on top of cloud computing, a concept that took over around 2006 when former Google CEO Eric Schmit mentioned it. Most cloud-based business models can be classified as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), or SaaS (Software as a Service). While those models are primarily monetized via subscriptions, they are monetized via pay-as-you-go revenue models and hybrid models (subscriptions + pay-as-you-go).
The explosion of the cloud has made computing power much much cheaper; thus, it turned into viable AI/ML applications that usually (at least at this point) necessitate a massive amount of data to run.
In this context, AI developers have become in greater demand!
Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.
With the other trends, DevOps and DevSecOps are highly requested, enabling companies to achieve continuous operations at scale.
DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.
In this context of increasing complexity, technology needs to be integrated into a whole businessmodel, business people with acumen in new technologies and integrating them within organizations.
A Business Engineer is a hybrid between a business administration and technology expert, a person with the business acumen and engineering abilities to understand a complex organization, devise solutions, and work as a liaison between commercial and technical teams.
At the same rate, business designers, able to integrate and fit technology within the value proposition and help organizations transition their business models, are also in high demand.
Business design enables organizations to deliberately craft a business model to prove sustainability in the marketplace by validating the building blocks of a businessmodel. The business designer can help an organization build a viable business model by readily testing its riskiest assumptions against the marketplace.
Data Scientists
AI Relevance: Data Scientists are integral to the AI industry because AI systems rely heavily on high-quality data to function effectively. They prepare, clean, and analyze datasets, which serve as the foundation for training machine learning and deep learning models. Data Scientists also develop statistical and predictive models that are crucial for uncovering insights and enabling AI systems to make data-driven decisions.
Key Responsibilities:
Data preparation, cleaning, and preprocessing for machine learning.
Designing and implementing predictive models and algorithms.
Analyzing large datasets to extract meaningful insights.
Collaborating with AI engineers to optimize models for real-world applications.
Industry Need: AI requires vast amounts of structured and unstructured data. Data Scientists ensure the availability of this data in a usable format, bridging the gap between raw data and actionable AI insights. Their work is essential for sectors such as healthcare (e.g., patient data analysis), finance (e.g., fraud detection), and retail (e.g., customer personalization).
Product Managers with Technical Expertise
AI Relevance: AI product development is complex, requiring a blend of technical understanding and market insight. Product Managers with technical expertise act as liaisons between AI engineers, data scientists, and business stakeholders. They ensure that AI-driven solutions are both technically feasible and aligned with business objectives, ultimately delivering value to end-users.
Key Responsibilities:
Defining product requirements that leverage AI capabilities.
Coordinating cross-functional teams to build and deploy AI-powered solutions.
Prioritizing features based on technical feasibility and customer needs.
Monitoring AI product performance and iterating based on feedback.
Industry Need: As businesses increasingly adopt AI, the demand for technically skilled Product Managers who can navigate the nuances of AI projects grows. These professionals are vital in industries such as e-commerce (e.g., AI recommendation systems), autonomous vehicles (e.g., sensor integration), and enterprise software.
Mobile App Developers
AI Relevance: Mobile apps are a primary interface for delivering AI capabilities to users, from virtual assistants to AI-driven fitness trackers. Mobile App Developers ensure that AI models and systems are efficiently integrated into mobile platforms, making them accessible, user-friendly, and responsive.
Key Responsibilities:
Developing mobile applications that integrate AI features, such as voice recognition or image processing.
Optimizing AI algorithms for mobile platforms to ensure efficiency and speed.
Collaborating with AI engineers to test and deploy machine learning models in apps.
Enhancing mobile app performance through user feedback and iterative design.
Industry Need: AI-powered mobile applications are becoming indispensable across industries such as healthcare (e.g., telemedicine apps with AI diagnostics), education (e.g., personalized learning apps), and entertainment (e.g., AI-powered streaming recommendations).
UX/UI Designers
AI Relevance: As AI systems become more prevalent, they often present complex data or perform tasks that require user interaction. UX/UI Designers create intuitive interfaces that make AI technology accessible and understandable to end-users, ensuring seamless integration into daily life.
Key Responsibilities:
Designing user-friendly interfaces for AI-driven systems, such as dashboards or chatbots.
Conducting usability testing to ensure effective interaction with AI features.
Simplifying the presentation of complex AI-driven analytics or insights.
Collaborating with AI developers to align interface design with system capabilities.
Industry Need: The AI industry needs UX/UI Designers to address challenges like user trust, transparency, and ease of use. These designers are particularly important for applications in healthcare (e.g., AI-powered diagnostic tools), finance (e.g., investment recommendation platforms), and enterprise software.
Edge Computing Developers
AI Relevance: AI systems increasingly require real-time decision-making capabilities, especially in latency-sensitive scenarios like autonomous vehicles or industrial automation. Edge Computing Developers design solutions that allow data to be processed closer to its source, reducing latency and improving efficiency.
Key Responsibilities:
Developing systems that process AI algorithms locally on edge devices.
Optimizing data transfer between edge devices and centralized systems.
Implementing edge AI solutions in IoT devices for real-time decision-making.
Ensuring the security and reliability of distributed AI systems.
Industry Need: With the proliferation of IoT devices and the need for decentralized AI processing, Edge Computing Developers are in high demand. Industries like manufacturing (e.g., predictive maintenance), healthcare (e.g., real-time patient monitoring), and transportation (e.g., autonomous vehicles) rely on edge computing to power AI-driven innovation.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
