As companies transition to AI-powered ecosystems, they must reassess current their current personnel, write thorough job description, and develop training plans to reskill their existing workforce. In the reassessment, a company may determine that they have a skills gap and need to recruit from outside. I will discuss some of the key AI positions and what existing roles may transform well to the new AI engagements.

 

11 Key AI-Powered Positions and Job Descriptions

 

Business Analyst

The business analyst is responsible for the problem definition, gathering the requirements, and identifying the success metrics of the project. This role is often done by the sales engineer who is customer-facing and can act as the domain expert. The one area for upskilling the sales engineer is on the AI development process, the different AI models, and the AI business value.

AI Strategist

The AI strategist will align the customer’s business objectives with an AI vision. They will define the AI project’s success criteria and how they are measured. This role is new to many companies who will need to take their more senior sales engineer personnel and train them on AI models, business problems that AI addresses, and how to measure those outcomes.

Project Manager

Companies already have project managers who can oversee AI development projects. These managers must be trained in AI-specific risks and development processes.

Configuration Management Specialist (CMS)

Like project managers, companies have CMS resources in place, but they need training to manage AI’s additional dimensions, including data sets and AI models.

Data Engineer

Responsible for data collection, cleansing, transforming, and database management, the data engineer will handle training, validation, and operational data sets, working closely with data scientists.

Data Scientist

This established role involves selecting the best AI model, training it with the training data set, validating it with the validation data set, and performing sensitivity tests by adjusting weights and calculations.

AI Ethics Officer

This new role is crucial for ensuring trust and explainability in AI applications, especially those with significant impacts on individuals. They should be engaged early in the development process.

ML Engineer

Companies often use senior software engineers as makeshift ML engineers. Formalizing this role involves deploying algorithms, developing APIs, and training experienced programmers in ML algorithms.

AI Cloud Architect

Like existing cloud architects, this role involves defining compute and memory requirements for AI applications and understanding AI software platforms from major cloud providers like AWS, Google, Azure, Oracle, and IBM.

Data Visualization Engineer (DVE)

This role involves prototyping, reviewing, and finalizing the UI, often using Power BI or Tableau, and working with ML engineers to visualize data for customers.

Software Engineer

Often a versatile role, the software engineer can address various application development areas and sometimes transition into ML or DVE roles.

 

Hiring or Upskilling?

In conclusion, the shift to an AI-powered ecosystem relies on re-evaluating existing talent and providing specific training. Companies need clear job descriptions and pragmatic training plans to help employees transition to new roles. Where talent gaps exist, external hiring will be necessary to fill those positions.