Navigating GenAI Hiring: 17 Roles You Need to Know About

Generative artificial intelligence (GenAI) has invigorated the data and analytics industry, creating a wave of new roles and transforming existing ones. As businesses attempt to harness the power of AI by creating GenAI related projects, understanding the key roles within this domain becomes crucial. Next week I will be at London Tech Week talking about the impact of AI and the tech skills shortage. In this blog I will give a sneak peak to some of those discussions by exploring the pivotal roles in AI, categorised as Must-have or Emerging, according to a recent Gartner graphic and research which is shown below:

Must-have AI Roles

• Data Engineer: Data engineers are the backbone of any GenAI initiative. They design, construct, and maintain scalable data pipelines that support data collection, transformation, and storage. Their work ensures that data is accessible, reliable, and ready for analysis.
• AI Architect: AI architects are responsible for designing the AI systems and frameworks that integrate with business operations. They define the architecture, select appropriate technologies, and ensure the system’s scalability and efficiency. Their role is critical in aligning GenAI solutions with business goals.
• Head of AI: The Head of AI oversees the entire AI strategy and implementation within an organisation. They lead GenAI initiatives, manage AI teams, and ensure that AI projects align with the company’s strategic objectives. Their leadership drives the successful adoption of AI technologies.
• UX Designer: User experience (UX) designers in AI focus on creating intuitive interfaces that make complex GenAI systems accessible and user-friendly. They work to ensure that AI applications are not only functional but also enhance the user experience through thoughtful design.
• Data Scientist: Data scientists are at the forefront of extracting insights from data. They apply statistical methods, machine learning, and AI techniques to analyse and interpret complex data sets. Their work drives data-driven decision-making and innovation within organisations.
  

  Emerging AI Roles

• Model Manager: Model managers oversee the lifecycle of GenAI models, from development to deployment and maintenance. They ensure models are performing optimally, manage versioning, and address any issues that arise during production.
  Model Validator: Model validators are responsible for the rigorous testing and validation of AI models. They ensure that models meet predefined performance criteria, are free from bias, and are reliable before being deployed in a production environment.
• ML Engineer: Machine Learning (ML) engineers bridge the gap between data scientists and the deployment of machine learning models. They focus on developing scalable ML solutions, optimising models for performance, and integrating them into existing systems.
• Prompt Engineer: Prompt engineers specialise in designing and refining the prompts used in natural language processing (NLP) models. They ensure that AI systems interpret and respond to user inputs accurately and effectively.
• Knowledge Engineer: Knowledge engineers develop and manage knowledge bases that support AI applications. They curate, organise, and structure information so that AI systems can access and use it efficiently.
• Analytics Engineer: Analytics engineers focus on creating data solutions that support advanced analytics and AI. They build data pipelines and analytical frameworks that enable the extraction of actionable insights from data.
• AI Product Manager: AI product managers drive the development of AI products from conception to launch. They work closely with cross-functional teams, including data scientists, engineers, and business stakeholders, to ensure that AI products meet market needs and business objectives.
• AI Risk and Governance Specialist: AI risk and governance specialists manage the ethical and regulatory aspects of AI implementations. They develop frameworks to mitigate risks, ensure compliance with laws and regulations, and promote responsible AI use within organisations.
• AI Ethicist: AI ethicists address the ethical implications of GenAI technologies. They develop guidelines and frameworks to ensure that AI applications are used responsibly and ethically, addressing concerns such as bias, privacy, and fairness.
• D&A and AI Translator: D&A (Data and Analytics) and AI translators bridge the communication gap between technical teams and business stakeholders. They ensure that AI solutions are understood, adopted, and utilised effectively within the organisation.
• Decision Engineer: Decision engineers design systems that support decision-making processes. They integrate AI and data analytics into business operations to improve decision quality and efficiency.
• AI Developer: AI developers are responsible for creating AI applications and solutions. They write code, develop algorithms, and implement AI models that solve specific business problems.

Lastly, some companies are trying to do mass full-time hiring to attract the skills mentioned above. This is costly and time consuming for most companies. Adopting both internal mobility and open talent models is a great way to add new skills to your company without adding more headcount.