2025 Artificial Intelligence/Machine Learning Job Descriptions Bundle - ResearchAndMarkets.com

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Machine Learning Job Descriptions Bundle" report has been added to ResearchAndMarkets.com's offering.

In current plans for technology deployment, over two-thirds of all companies looking at Artificial Intelligence (AI) and Machine Learning (ML) as a priority. A limiting factor is finding individuals with the skills and experience to meet those goals.

Universities, over time, have provided graduates that meet the needs of businesses to meet their technological needs resources currently are not geared toward providing their graduates with the tools they need to be viable candidates for these positions.

The challenge faced by CIOs and recruiters is to define who should be on the AI ML team. Adding to the complexity of the problem, the requisitions and job descriptions that are being used are insufficient and inaccurate.

Job Descriptions for AI Ml IT Pros

We have found that the skills and experience that are required need to be included in job descriptions and open requisitions for Artificial Intelligence and Machine Learning team members.

• AI / ML focused Executive Management
• Mathematical and Statistical Leader
• AI and ML Tool Expert
• AI and Security Expert
• Large Language Models (LLMs) Expert

Few individuals have direct working experience to be able to leverage this immediately. What is required is a team that needs to be built that can achieve these objectives. Team skills have to include as many of these as possible.

AI / ML focused Executive Management

There needs to be an executive responsible for overseeing a company's overall strategy, acquisition, implementation, and monitoring of AI and Machine Learning (ML) technology. This role requires a deep understanding of the business, technical expertise, and regulatory awareness. They must be capable of communicating AI-related information effectively across the organization. However, the role is not solely technical; it demands diverse skills, including AI ethics, understanding, and implementation awareness.

The most mature and most data-fluent organizations are now using data to determine new business opportunities and new products to develop as well as how to be more efficient, more productive, and more competitive. This executive's purpose is to break down barriers that remain between IT, the data function, and the business units.

They are not only an AI expert but also a seasoned operational manager. As the role is transformational. The executive is responsible for the adoption of AI and machine learning across the entire business. As with most senior executive titles, the responsibilities are set by the organization's board of directors or other authority, depending on the organization's legal structure. They are responsible for AI across the entire enterprise and its operations.

Mathematical and Statistical Leader

There needs to be an individual who advocates, evangelizes, and can build data-fuelled products that drive AI/ML solutions The individual needs to be detail-oriented and is an expert on the data available or possible. They need to provide insight into leading analytic practices, designs and leads iterative learning and development cycles, and ultimately produces new and creative analytic solutions that will become part of the enterprise's core deliverables.

They need to drive initiatives to improve the monetization of the enterprise's AI and ML applications including experience while delivering AI and ML efforts to better understand how to engage with the enterprise's Imp;lamentations of AI and ML.

AI and ML Tool Expert

There needs to be an individual who deals with algorithms and codes that can train a machine to behave like a human. One of their primary responsibilities is to be well-versed in applied math, probability, statistics, and similar topics so they can successfully and efficiently program machines. They are skilled in aspects of computer and web-based programming, user experience (UX) design, project management, and client communication.

Their focus is on both the front and back ends of AI/ML applications. This includes underlying database work, user-facing construction, and client-focused communication aimed at the planning and maintenance of projects.

They are responsible for developing innovative solutions to challenging problems, including command and control and high-integrity solutions. Perform complex analysis, design, development, testing, and debugging of computer software for distinct product hardware or technical service lines of businesses. Perform AI/ML design, operating architecture integration, and computer system selection. Operate on multiple systems and apply knowledge of one or more platforms and programming languages.

AI and ML Security Expert

• An AI security expert to handle the unique challenges, such as ensuring adherence to regulations, data transparency, and internal vulnerabilities that come with AI models and algorithms, including adversarial attacks, model bias, and data poisoning. Role needs to include:
• Understand the AI ML system life cycle in a production setting: from data collection, and data processing, to model deployment. This includes backup, recovery, and contingency planning related to AI systems.
• Outlines machine learning threats and recommendations to abate them. To directly help engineers and security professionals, we enumerated the threat statement at each step of the AI ML system-building process.
• Enables organizations to conduct risk assessments. The framework provides the ability to gather information about the current state of security of AI systems in an organization, perform gap analysis, and track the progress of the security posture.

Large Language Models (LLMs) Expert

Prompt engineers who can craft and improve text queries or instructions (prompts) in large language models (LLMs) to get the best possible answers from genAI tools. An LLM is a machine-learning neuro network trained through data input/output sets; frequently, the text is unlabeled or uncategorized, and the model uses self-supervised or semi-supervised learning methodology. Information is ingested, or content entered, into the LLM, and the output is what that algorithm predicts the next word will be. The input can be proprietary corporate data or, as in the case of ChatGPT, whatever data it's fed and scraped directly from the Internet.

Training for LLMs to use the right data requires an individual versed in the tool and data.

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