5 emerging tech roles revolutionising industries

The past decade has been a time of unprecedented technological change. The use of artificial intelligence (AI) has increased exponentially, and it is estimated that by 2025, the global artificial intelligence market will reach USD190.61 billion.

AI is applied in a lot of fields, such as, in disease diagnosis in hospitals to increase efficacy and improve patient outcomes. Applications of blockchain, on the other hand, are also growing beyond cryptocurrencies. For example, 81 of the top 100 public companies now use blockchain, mainly in the financial sector, where smart contract languages and decentralised finance (DeFi) are transforming traditional models.

While these, among other transformational emerging technologies, are growing in their adoption pace, they are changing industries in ways unlike ever before. Companies should consider keeping up with the rapidly changing tide of technology or risk falling behind.

Emerging tech roles like AI engineers and blockchain developers are becoming crucial for innovation and maintaining a competitive edge. These new technology jobs are driving advancements in automation, data analysis, and secure digital transactions for businesses to stay ahead in an increasingly digital and competitive landscape.

Tech specialists reshaping business models

Here are five key emerging tech roles, their importance, industry-specific applications, career pathways, and ways for organisations to position themselves for success in this evolving landscape.

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AIGC (AI-Generated Content) Engineer

AIGC engineers are responsible for developing and implementing AI systems that generate human-like content, transforming traditional content workflows.

Key responsibilities:

• Design and implement AI models for content creation using machine learning frameworks, such as TensorFlow or PyTorch.
• Fine-tune the AI models to align with the brand’s voice, tone, and style across various platforms.
• Make transparency and the mitigation of bias a core part of all AI-assisted content creation.
• Collaborate with creative teams for the integration of AI systems into existing content management platforms (CMS) and tools.

Required skills:

• Proficient in machine learning frameworks.
• Strong understanding of natural language processing (NLP), also known as understanding human language, and computer vision.
• Familiar with content management systems and brand communication strategies.
• Soft skills include creativity, collaboration, and the ability to explain technical concepts to non-technical stakeholders.

Job challenges:

AIGC engineers face the dual challenge of ensuring high-quality content aligned with the brand’s voice while mitigating biases inherent in AI-generated content.

They need to strike a balance between automation and human oversight, ensuring ethical standards and transparency, especially for sensitive or regulated industries like healthcare and finance.

Increased reliance on automation requires AIGC engineers to seamlessly blend AI-driven content with creative direction. This balance ensures content remains engaging and aligned with human creativity, even in highly automated workflows.

Career trajectories:

Typically, entry into this field requires a computer science, engineering, or machine learning background, with an exceptionally heavy emphasis on natural language processing and content strategy.

Aspiring AIGC engineers will further benefit from training in creative and branding skills with the power to bridge technical ability and creative direction. Certifications in machine learning can help enhance job prospects, while hands-on experience with AI tools like GPT-4 or DALL-E can set candidates apart.

Industry impact:

AIGC engineers are transforming content-heavy industries like marketing, media, and e-commerce. Fashion retailers use AI to deliver personalised style recommendations, while media companies automate video creation for faster content production.

Brands like Nike and Coca-Cola integrate AI tools to automate ad copy and visuals, boosting efficiency. BuzzFeed automates quizzes and personalised articles using AI-generated content, and Alibaba employs AI to generate product descriptions across its platform, saving time and maintaining brand consistency.

What’s next for this role:

As generative AI continues to evolve, new areas such as video content generation, virtual influencers, and AI-driven storytelling are expected to see significant growth.

The role of AIGC engineers will likely expand to include the development of immersive AI experiences that blur the lines between digital and physical content creation.

With the global adoption of AI in marketing and media sectors accelerating, companies must stay adaptable as new platforms emerge, such as deepfake detection tools and AI-based creative assistants.

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AI Product Manager

AI product managers guide the development of AI-powered solutions, ensuring they meet both business goals and customer needs while adhering to ethical standards.

Key responsibilities:

• Define the AI product vision and strategy, aligning it with overall business goals.
• Collaborate with data scientists, engineers, and stakeholders to identify customer pain points and develop AI-driven solutions.
• Manage the end-to-end product lifecycle from ideation to deployment to ensure that AI features are delivered on time and within scope.
• Ensure ethical AI development, taking responsibility for reducing bias, data privacy, and transparency in AI models.
• Conduct market research to keep up with AI trends and identify opportunities for new AI features or improvements.

Required skills:

• Proficiency in machine learning principles and an understanding of AI development processes.
• Ability to translate business problems into AI solutions and vice versa.
• Strong project management skills, with experience managing cross-functional teams.
• Excellent communication skills to liaise between technical and non-technical stakeholders.
• Knowledge of ethical AI standards and regulatory frameworks.

Job challenges:

AI product managers face the challenge of managing AI models’ complexity while ensuring they adhere to ethical guidelines. These managers must tackle bias in AI models, handle regulatory requirements, and ensure data privacy across various use cases.

AI product managers also need to balance innovation against customer trust by being open about how AI makes decisions. They must also adapt to evolving technologies and market demands, adjusting product strategies to ensure AI solutions remain competitive and relevant. The ability to pivot in response to emerging trends is crucial for long-term success.

Another problem is navigating cross-functional collaboration between data science teams and business departments, as most technical limitations need to be worded in simpler, not-so-technical non-terms to the leadership and stakeholders.

Career trajectories:

An AI product manager role typically requires a blend of technical expertise and business acumen. Most AI product managers come from product management backgrounds and supplement their technical skills with data science and machine-learning training.

Completing certifications like AI product management or machine learning for product managers can help professionals transition into this role.

AI product managers can progress to head-level positions, such as the Head of AI Products or Chief Product Officer, where they can manage more key strategic AI-driven portfolios within the company.

Industry impact:

AI product managers are crucial in industries like fintech and e-commerce, where they develop AI tools for tasks such as fraud detection, credit assessment, and personalisation engines that enhance customer experiences.

Companies like Stripe, Amazon, and Spotify rely on AI product managers to create innovative solutions. Stripe has significantly reduced fraud rates with AI-powered detection systems, while Amazon continues to innovate with features like voice-activated shopping on Alexa, and Spotify refines recommendation algorithms to personalise user experiences.

What’s next for this role:

As AI continues to evolve, AI product managers will lead the integration of AI into mainstream products and services. The growing focus on AI ethics and algorithmic transparency will require them to stay updated on privacy laws and ethical standards.

Their role is expected to expand into managing multi-modal AI systems that combine text, speech, and vision. The rise of AI in the Internet of Things (IoT) will present new opportunities for AI product managers to develop innovative, connected solutions.

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Algorithm Engineer

Algorithm engineers design and optimise algorithms for applications like recommendation engines. They bridge the gap between data science and software engineering.

Key responsibilities:

• Design and implement algorithms to process, optimise, and analyse data.
• Collaborate with data scientists to turn statistical models into production-ready algorithms.
• Ensure algorithms are optimised for performance and scalability, especially when dealing with large datasets.
• Conduct algorithm testing and debugging to improve accuracy and reduce computational costs.
• Work across teams to integrate algorithms into existing software platforms and systems.

Required skills:

• Strong understanding of algorithms and data structures.
• Expertise in programming languages such as Python, Java, or C++.
• Familiarity with machine learning models and optimisation techniques.
• Deal with massive data sets and make the algorithm more efficient.
• Experience working with cloud computing platforms for algorithm deployment.

Job challenges:

Algorithm engineers face data privacy and security challenges, particularly in industries like finance and healthcare, where sensitive data is involved. Another key challenge is ensuring algorithms can scale to handle growing datasets without sacrificing performance.

Balancing the trade-offs between algorithm accuracy and efficiency is crucial, as more complex algorithms may provide better results but require more computational resources.

Career trajectories:

Algorithm engineers usually come from the computer science or mathematics field and have a strong background in data structures, probability theory, and optimisation techniques.

They can advance into lead engineer or data science manager roles, overseeing larger algorithmic systems or managing data teams.

Additional training in advanced algorithms or machine learning from platforms like MIT OpenCourseWare can strengthen career prospects, while experience with cloud platforms like AWS or Google Cloud for scaling algorithms is becoming increasingly valuable.

Industry impact:

Algorithm engineers have a significant impact across sectors like logistics and finance. In logistics, they design algorithms to optimise delivery routes, improving efficiency and reducing costs.

In finance, they develop models for predicting market trends and automating trading strategies, enabling faster decision-making. Companies like UPS and Netflix rely on these engineers to optimise operations, while Amazon and Goldman Sachs use algorithms for real-time data processing, from delivery route optimisation to high-frequency trading.

Algorithm engineers are vital in developing AI-driven personalisation systems, especially in e-commerce and entertainment. These systems enhance customer experiences by delivering tailored recommendations based on user preferences and behaviour.

What’s next for this role:

As quantum computing emerges, algorithm engineers will need to adapt their skills to leverage quantum algorithms, which have the potential to solve problems exponentially faster than traditional algorithms.

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Blockchain Developer

Blockchain Developers design and implement blockchain technologies, making them practical for real-world applications across industries. As blockchain continues to mature, developers are creating decentralised applications (DApps), also known as apps that run on a blockchain network, and smart contracts that are reshaping industries like finance, supply chain, and healthcare.

Key responsibilities:

• Design and develop blockchain architectures for secure and efficient decentralised systems.
• Build decentralised applications (DApps) on platforms such as Ethereum and Hyperledger.
• Ensure blockchain systems are safe, efficient, and dependable.
• Integrating blockchain solutions with existing business systems, ensuring seamless and secure data exchange.
• Keeping up with advancements in cryptography and blockchain protocols to implement the most up-to-date security measures.

Required skills:

• Proficiency in blockchain platforms like Ethereum and smart contracts languages like Solidity.
• Expertise in cryptography and distributed ledger technology.
• Ability to develop decentralised applications (DApps).
• Familiarity with security standards in blockchain networks.

Job challenges:

Scalability remains a significant challenge for blockchain developers. A key focus is ensuring that blockchain networks can handle large volumes of transactions without compromising speed or security. Developers must also navigate the evolving regulatory landscape, especially in finance, where laws governing blockchain technology are still being shaped.

Developers must also tackle interoperability issues, ensuring seamless integration between various blockchain networks. This is especially critical as more industries seek to adopt decentralised systems that communicate across multiple platforms.

Career trajectories:

Blockchain developers come from software engineering or cryptography backgrounds. Experience with blockchain platforms like Ethereum, Solana, or Hyperledger and knowledge of smart contract languages like Solidity is essential.

As the field grows, developers can specialise in particular blockchain applications, such as DeFi or NFT development, or advance into roles like blockchain architect or lead developer. Certifications from platforms like Consensys Academy or Blockchain Council can help developers stay competitive.

Industry impact:

Blockchain developers are transforming industries like supply chain management by enabling item tracking, reducing fraud, and improving transparency. In finance, blockchain supports DeFi, allowing peer-to-peer transactions without intermediaries.

Companies such as IBM and Accenture are at the forefront of enterprise blockchain solutions. IBM’s Food Trust blockchain enhances transparency and traceability in the food supply chain, while Aave and Compound use blockchain to facilitate decentralised lending, bypassing traditional financial intermediaries.

LLM engineers also focus on developing real-time AI processing capabilities, making models more interactive and adaptable for industries like customer service, healthcare, and finance. These advancements allow businesses to respond quickly to evolving customer needs and data.

What’s next for this role:

The emergence of Web3 and DeFi is expected to change the development of blockchain in the coming years. In addition, they will need to be informed of enhancements in the related field. The tokenisation of assets, NFTs, and cross-chain interoperability are future trends shaping blockchain technology.

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LLM (Large Language Model) Engineer

LLM engineers build, train and tune AI models that drive applications like chatbots and summarisation tools, transforming industries from customer service to healthcare.

Key responsibilities:

• Design and train large-scale language models using frameworks such as Hugging Face Transformers and OpenAI GPT.
• Adapting models to suit industry-specific requirements, ensuring that the models serve both business needs and applications.
• Optimise model performance by reducing computational inefficiencies and improving the efficiency of inferences.
• Address critical challenges such as bias, inaccuracy, and data privacy in language models.
• Ensuring LLMs are used more ethically by implementing fairness strategies.

Required skills:

• Proficiency in NLP frameworks and machine learning libraries such as TensorFlow and PyTorch.
• Strong understanding of linguistic principles, tokenisation, and language model architecture.
• Experience working with large datasets and fine-tuning LLMs for specialised industry tasks.
• Soft skills, including the ability to collaborate across teams and explain complex technical concepts to non-technical stakeholders.

Job challenges:

The scale and complexity of large models pose a significant challenge for LLM engineers. These models are slow to train and demand substantial computational resources for deployment, which can lead to inefficiencies.

Besides that, intervention of bias in training data is needed, which, if not treated, will lead to one-sided or offensive output. More importantly, ensuring data privacy and complying with regulatory frameworks, such as GDPR, is critical, especially when LLMs are used to process sensitive information.

As AI models become more commonly used in industries like healthcare and finance, the responsibility for ethical and compliant deployment becomes even more crucial.

Career trajectories:

Professionals entering this field typically have backgrounds in natural language processing, computational linguistics, or computer science. LLM engineers often possess advanced degrees in these areas, along with specialised knowledge in machine learning, deep learning, and data science.

Certification in machine learning, like from DeepLearning.AI or Google AI, is an added advantage for the aspiring engineer.

As the demand for AI-driven solutions continues to grow, professionals with a deep understanding of NLP frameworks like BERT, GPT-4, or T5 are highly sought after. Hands-on experience with large-scale model deployment in cloud platforms like AWS Sagemaker or Google Cloud AI is also valuable in this career.

Industry impact:

LLM engineers are revolutionising industries by accelerating and improving text processing. LLM-driven chatbots handle real-time interactions in customer service, enhancing the customer experience.

In legal tech, AI tools summarise complex documents, reducing manual review time, while healthcare benefits from automated patient communication and medical record management.

Large companies like IBM and Microsoft use LLMs for advanced data insights and document processing. OpenAI’s ChatGPT has transformed customer service with automated, human-like responses, reducing support costs. J.P. Morgan's Quest IndexGPT leverages GPT-4 to generate keywords for thematic stock indices, covering areas like AI, cloud computing, and renewable energy.

What’s next for this role:

As LLMs evolve, their integration into business tools will increase, with engineers focusing on improving model efficiency, reducing computation costs, and enabling real-time processing.

The growing emphasis on AI ethics and data privacy requires LLM engineers to stay ahead of regulatory changes. Emerging trends like multimodal models that combine text, images, and video will also push engineers to expand their skill sets, driving further innovation across industries.

Edge computing is opening up new opportunities for real-time, decentralised AI processing, making the role of LLM engineers more dynamic and future-proof.

Cross-cutting themes and the future of work in the tech industry

Several common themes run through all these emerging tech jobs:

• Interdisciplinary knowledge: These roles require technical expertise, industry-specific knowledge, and business acumen. Professionals need to understand how their work fits into business strategies.
• Ethical and regulatory considerations: With increasing influence comes greater ethical responsibility.  Assuring fairness, minimising bias, and adhering to constantly evolving regulations are the main concerns here.
• Continuous learning: With technology evolving rapidly, professionals in these roles must commit to lifelong learning. Continuous development is essential, whether through formal education or staying updated on industry trends.
• Collaboration: These roles often involve cross-functional teams, requiring strong communication and teamwork skills to translate complex technical concepts into practical business solutions.

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How organisations can prepare for emerging tech jobs

To position your business for success:

• Invest in training: Upskill your workforce by offering training and opportunities for continuous learning.
• Foster innovation: Create an environment that encourages experimentation and rewards calculated risk-taking.
• Build partnerships: Collaborate with universities and tech communities to stay ahead of the curve and tap into new talent.
• Revise hiring practices: Look beyond traditional qualifications and focus on adaptability, problem-solving abilities, and willingness to learn.

Embrace the future

The rise of tech jobs like AIGC engineer, LLM engineer, AI product manager, algorithm engineer, and blockchain developer marks a turning point in the tech industry. Businesses that recognise and embrace these roles will be better positioned to drive innovation and maintain their competitive edge. Agile and regularly re-evaluating talent needs is how forward-thinking companies create a sustainable path to growth in a digitalising world.

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