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Jobs of the future, now hiring: AI job titles and what they do

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The expansion of large language models—such as ChatGPT, BERT, Bard, and LLaMA—has kick-started the conversation about how these AI tools can be incorporated into business operations across industries. As companies strive to tap into the immense potential of natural language processing, a host of new AI job titles, including language model specialists, prompt engineers, and API integration experts, have emerged. In this post, we explore the responsibilities of each of these roles and how they can fit into an organization’s overarching tech environment.

AI job titles that are in high demand

While we’re just scratching the surface of AI roles, here are six common jobs available in the field.

1. Language Model Trainers

AI-generated image of language model trainers.
AI-generated image of a Language Model Trainer.

Language model trainers form the backbone of the language model ecosystem. These specialists employ their expertise to fine-tune pre-trained AI models, aligning them with specific business requirements. Language model trainers curate and develop high-quality datasets that facilitate fine-tuning, ensuring models comprehend industry-specific jargon, context, and subtleties. Language model trainers possess a deep understanding of the underlying principles and capabilities of large language models.

Desired qualifications for Language Model Trainers could include:

  • Advanced degree in a STEM field or 5+ years of relevant experience
  • Excellent proficiency in the company’s language of operation (English, Spanish, etc.), and experience working in the company’s industry
  • Experience working with deep learning models using real-life (“industry”) data
  • Excellent proficiency in Python programming and one or more deep learning libraries (PyTorch, TensorFlow, JAX, etc.)
  • Background in software engineering
  • Experience with building and/or fine-tuning large language models, as well as traditional machine learning models
  • Ability to translate business requirements into technical action items

2. Natural Language Processing Engineers

AI-generated image of an NLP Engineer.
AI-generated image of an NLP Engineer.

Natural Language Processing (NLP) engineers design and implement the models and tools that translate natural language into programming language, and vice versa. Working closely with data scientists, software engineers, and domain experts, NLP engineers integrate their models into existing systems and develop novel applications. NLP engineers also collaborate with language model trainers to ensure models are appropriately fine-tuned for specific use cases. By enabling the smooth integration of language models into diverse business processes such as customer support, content generation, and data analysis, NLP engineers play a pivotal role in driving operational efficiency and innovation.

Desired qualifications for NLP Engineers include:

  • BS in Computer Science or related field
  • Good knowledge of classical machine learning techniques
  • Programming expertise with Python or R and relevant libraries
  • Strong understanding of language processing methods such as probabilistic language modeling, part-of-speech tagging, and Named Entity Recognition (NEM).
  • Strong background in statistical modeling
  • Experience leveraging cloud-based architectures and technologies to deliver optimized ML models at scale
  • Familiarity with CI/CD best practices

3. Ethical AI Specialists

An AI-generated image of an Ethical AI Specialist
AI-generated image of an Ethical AI Specialist.

Ethical AI specialists take charge of addressing the ethical implications surrounding language model deployment. They establish guidelines and best practices to ensure responsible usage, mitigating risks such as bias, misinformation, and privacy concerns. Ethical AI specialists work closely with language model trainers, NLP engineers, cyber security engineers, and prompt engineers to identify and rectify potential biases or risk factors in training data and model outputs. Collaborating with legal and compliance teams, they ensure language models adhere to relevant regulations and industry standards, promoting ethical and transparent practices. Depending on a given company’s organizational structure, this role could function in a highly technical capacity (ie. involved in hands-on development), or as more of an advisory/oversight position.

Desired qualifications for Ethical AI Specialists could include:

  • Minimum of a Bachelor\’s degree in a relevant field
  • Hands-on development experience of AI/ML models, or strong AI/ML product management experience with detailed understanding of the entire product lifecycle
  • Strong knowledge of industry specific ethical codes and compliance frameworks (HIPAA, SOC 2, etc)
  • Experience with code testing and auditing procedures
  • Ability to obtain and maintain a security clearance
  • Up to date knowledge of recent developments in the field of natural language processing, machine learning, and deep learning
  • Up to date knowledge of current research in the field of AI ethics and policies (including algorithmic fairness, accountability, transparency, and privacy-enhancing technology)

4. Prompt Engineers

An AI-generated image of Prompt Engineers.
AI-generated image of Prompt Engineers.

Prompt engineers have emerged as an essential role in maximizing the value of language models. These professionals specialize in creating effective prompts and instructions that guide language models to produce desired outputs. They possess a deep understanding of the nuances and capabilities of specific language models, allowing them to design prompts that yield accurate and contextually relevant responses. Prompt engineers play a vital role in optimizing the performance of language models, improving their output quality, and enabling seamless interactions with end-users. To accomplish this, the prompt engineers collaborate closely with data engineers, data scientists, and product managers. They will often be positioned on a company’s engineering team, but likely serve in a consulting capacity across departments. In some companies, the prompt engineer could present as an entirely new role. In others, the relevant responsibilities could be incorporated into the job duties of existing data engineers, ML engineers, and NLP engineers.

Desired qualification for prompt engineers could include:

  • BS, MS, in Computer Science, Computational Linguistics, or a related field, or equivalent real-world experience
  • Strong theoretical background in deep reinforcement learning, and familiarity with NLP and ML models and methodologies
  • Strong understanding of ML workflows: data sampling and curation, pre-processing, model training, evaluation, deployment, and optimization
  • Familiarity with big data technologies and cloud based data environments
  • Experience working with both structured and unstructured data sources
  • Experience with designing and developing AI prompts using large language models, including GPT-3 or ChatGPT and other solutions, including Lex or Rasa
  • Experience with refining general purpose language models for specific applications

5. Language Model Product Managers

AI-generated image of a Language Model Product Manager.
AI-generated image of a Language Model Product Manager.

Language model product managers play a crucial role in bridging the gap between technical expertise and business objectives. These professionals have a deep understanding of both the capabilities of language models and the needs of the company. They work closely with stakeholders to identify use cases, define requirements, and prioritize features for language model applications.

Language model product managers are responsible for ensuring that language model projects align with the strategic goals of the organization. They collaborate with language model trainers, NLP engineers, and ethical AI specialists to deliver innovative solutions that meet customer needs while considering technical feasibility and ethical considerations.

Desired qualifications for language model product managers could include:

  • Bachelor\’s degree in Computer Science, Engineering, Data Science, Business Administration, or a related field
  • Product Management at a technology company, ideally working on products with ML/AI based products
  • Deep understanding of ML tech stacks for building, deployment, customization, and integration.
  • Ability to communicate well with both technical and non-technical colleagues and clients
  • Team leadership experience
  • Industry specific experience to help guide the tailoring of products for business users
  • Analytical thinking skills for data driven decision making and defining/measuring success metrics

6. API Integration Experts

AI-generated image of an API Integration Expert.
AI-generated image of an API Integration Expert.

API integration experts play a critical role in enabling the integration of large language models into existing business systems and workflows. These professionals specialize in leveraging APIs (Application Programming Interfaces) to connect and interface language models with organizational software and applications. They are responsible for designing and implementing the necessary code and infrastructure required for seamless communication between the language models and other systems. With the proper API integration, companies are able to extend the utility of language models across different domains and applications.

Desirable qualifications for API integration experts could include:

  • BS/BA in Computer Science, or equivalent experience
  • Strong knowledge of EAI/SOA best practices
  • Familiarity with API management platforms and how to implement security policies, traffic management, and other protocols that support the scaling of APIs across the organization
  • Knowledge of modern APIs (SOAP and REST) & API Tools
  • Knowledge of Single Sign On configuration and testing
  • Experience with API monitoring tools to assess integration performance
  • Proficiency with at least one programming language (Python, Javascript)
  • Experience with workflow automation tools
  • Ability to assess data access and privacy concerns

AI job titles may be new, but they build on existing skills.

While these roles may present as entirely new developments in the field, each will build on existing skills and technologies that already form the building blocks of companies’ data environments. At the same time, tech professionals wishing to shift into one of these newly evolving roles will also need to demonstrate the ability to think outside the box in order to participate in the process of innovation. Companies will need to evaluate both of these qualities as they build their new large language model-focused teams.

If you’re looking for help hiring an experienced artificial intelligence developer, large language model trainer, prompt engineer, or any other ai-focused role, contact our data science recruiters today.

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