How to write a generative AI engineer job description

Given the rapid evolution of generative AI technology, writing a job description for a generative AI engineer can be uniquely challenging. The required skill set for the role is constantly changing by the nature of evolving tech. 

Distinguishing between essential and preferred skills is also tricky because the technology and its applications are so specialized. A great job description must also look beyond technical skills and appeal to the highly creative individuals in the field.

In this article, we delve into how to navigate these challenges and write an effective job description that appeals to the best generative AI engineers.

What is a generative AI engineer?

A generative AI engineer specializes in designing, developing, and managing artificial intelligence (AI) systems that can autonomously “generate” content, such as text, images, and music. 

These engineers leverage deep learning and machine learning techniques to train AI models on large datasets, enabling these models to generate new, original content that mimics human-like creativity. 

Oriol Vinyals, a Google research scientist, says that “generative models…have the potential to transform industries from media to finance to healthcare.” 

Generative AI engineers’ expertise lies in algorithm optimization, data modeling, and applying AI to solve complex, creative challenges.

Key skills to look for in a generative AI engineer

Generative AI engineers should possess a particular mix of skills to perform well in their roles. Read on for the primary hard and soft skills that you should look out for in candidates.

Hard skills

• Advanced programming knowledge, including mastery of programming languages such as Python, and especially AI-centric libraries like TensorFlow, PyTorch, and Keras. This includes the ability to implement and manipulate complex algorithms fundamental to developing generative AI models.

• Expertise in generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The candidate should be able to design, train, and optimize these models to generate high-quality, creative content.

• Natural language processing (NLP) for text generation projects. This includes familiarity with techniques for text parsing, sentiment analysis, and the use of transformers like GPT (generative pre-trained transformer) models.

• Data management knowledge, including data pre-processing, augmentation, and generation of synthetic data. This involves cleaning, labeling, and augmenting data to train and improve AI models.

• Cloud computing and deployment knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. This includes understanding containerization technologies like Docker and orchestration tools like Kubernetes, which are important for scaling AI solutions.

Soft skills

• Problem solving for thinking outside the box to design and implement novel AI solutions when faced with unprecedented challenges.

• Analytical thinking for assessing model performance, interpreting outcomes, and making data-driven decisions for model improvement.

• Collaboration and communication for articulating technical details and project needs to technical and non-technical team members, including data scientists, software developers, and business stakeholders.

• Continuous learning for keeping up the latest research, tools, and techniques in the rapidly evolving generative AI landscape.

• Patience and persistence for staying motivated, even when facing setbacks.

How to write an effective generative AI engineer job description

Use these four best practices to write an accurate and detailed generative AI engineer job description that attracts qualified applicants to your company.

1. Highlight specific model experience

When drafting a skills-based job description for a generative AI engineer, you must specify the types of generative models and technologies candidates should have experience with.

For example, explicitly state if the role requires expertise in GANs, VAEs, or transformer models like GPT-4 for text generation tasks.

2. Outline data handling capabilities

Emphasize experience in managing and manipulating large datasets, including creating synthetic datasets.

Specify the types of data the engineer will work with (e.g. image, text, or audio) and the expected competencies in data preprocessing, augmentation, and analysis.

Here’s an example requirement: "Experience in preprocessing large image datasets for training GANs, including techniques for data augmentation and synthetic data generation to enhance model training outcomes."

3. Focus on project lifecycle expectations

Detail the expectations for candidates to manage projects from conception through deployment, including model training, validation, and scaling. 

Describe the types of projects they'll lead or contribute to, emphasizing the end-to-end management of AI solutions.

Here’s what this could look like: "Seeking candidates who can lead generative AI projects from initial model design through to deployment, including scaling models using cloud services and optimizing for performance and cost."

4. Emphasize ethical AI development

Stress the importance of ethical considerations in AI development, especially in generative AI, where issues like bias and misinformation can arise. 

Ask for familiarity with ethical AI practices, including designing models that are fair, transparent, and respectful of privacy. 

For example, you might say that "applicants must demonstrate an understanding of ethical AI principles in the context of generative models, including strategies for mitigating bias and ensuring the responsible use of AI technologies."

Generative AI engineer job description template

Attract top engineers with a compelling job posting by customizing our generative AI engineer job description template.

Company introduction

Discuss your company's core values, missions, products and services, and goals. Describe how your company uses generative AI and the impact the engineer will have on operations and business objectives. 

If your company is an industry leader, mention any significant breakthroughs or awards that demonstrate this. If the AI engineer is going to work on a major or groundbreaking project, state this to make your company more appealing to top engineers.

Benefits of working with [your company]

List the benefits for generative AI engineers at your company. Include unique perks, like training programs and certifications related to AI and data modeling, policies to support work-life balance, and a work culture that values innovation and creativity.

Generative AI engineer job brief

[Company name]

Job title: Generative AI Engineer

Reports to: [Job title of direct supervisor/manager]

Position type: [For example, full-time or part-time]

Location: [For example, remote, hybrid, or on-site (include address if relevant)]

Salary and benefits: [Include competitive salary, health insurance, retirement plan, etc.]

Responsibilities and duties

• Design and develop advanced generative models, such as GANs and VAEs, for [specific application – for example, content creation or data augmentation].

• Collaborate with other teams to integrate AI solutions into [particular projects or products].

• Manage the project lifecycle from research and development to deployment and optimization.

• Stay updated on and contribute to the latest advancements in AI research, applying new findings to ongoing projects.

• Ensure ethical AI development practices, prioritizing fairness, transparency, and privacy.

Skills and qualifications

Required skills and knowledge:

• Strong proficiency in Python and AI frameworks (TensorFlow, PyTorch)

• Demonstrated knowledge of generative models (GANs, VAEs) and NLP

• Solid understanding of machine learning algorithms and data pre-processing techniques

• Knowledge of cloud computing and AI deployment (AWS, Google Cloud)

• Excellent problem-solving, analytical, and creative thinking skills

Preferred skills and experience:

• Degree in computer science, AI, or related field

• Publications or contributions to AI research or open-source projects

• Strong communication skills for effective teamwork and stakeholder engagement

• Experience with ethical AI practices in AI models

3 things to avoid when writing a job description for a generative AI engineer

Be sure to get the best candidates through the door. Avoid these three common errors when writing your generative AI engineer job description to ensure you attract the right people. 

1. Vagueness about project details

Don’t be vague about the types of projects the generative AI engineer will work on. Generic descriptions fail to capture the imaginations and interest of top talent. 

Instead, provide examples of actual or hypothetical projects, such as "developing AI to generate realistic 3D environments for virtual reality applications" or "creating an AI system for generating personalized content in real-time."

2. Not asking for a portfolio

A portfolio can provide concrete evidence of a candidate's abilities in generative AI. Not asking for a portfolio of relevant projects or contributions to open-source projects can result in missing out on practical insights into candidates' expertise and creativity. This might mean you overlook candidates who lack certain qualifications but would otherwise exceed in your role.

3. Ignoring deployment and scalability

Not asking for knowledge of deploying AI models into production environments and scaling them according to user demand can lead to hiring unqualified candidates. List expectations for knowledge in containerization tools, orchestration, and cloud services for managing AI applications at scale to avoid this.

Next steps: Attracting and assessing generative AI engineer candidates

After writing and posting your job description to find generative AI engineer candidates, you must thoroughly assess each applicant's technical knowledge, skills, and personality traits.

TestGorilla is a talent assessment platform that provides a robust and effective method to evaluate candidate skills. Here are some of the specific tests we offer for generative AI engineers:

• Working with generative AI test to assess candidates' understanding and application of open-source generative AI

• Artificial intelligence test that checks candidates' proficiency in core AI concepts and ability to solve situational AI tasks

• Machine learning test, covering core machine learning concepts like classification and regularization

• PyTorch test to assess candidates' knowledge of PyTorch fundamentals, neural architectures, network architectures, and more

• Critical thinking test to evaluate how candidates use analytical skills to make judgments using the given information

Apart from role-specific skills, you can also test soft skills through personality and culture, cognitive ability, and situational judgment tests.

With hundreds of tests in our library to choose from, you can tailor your assessments to match the exact skills and expectations of the generative AI engineer at your organization. 

Combine up to five tests to create a talent assessment, and add custom questions to each test to personalize the assessment even more.

FAQs

Finally, let’s cover some answers to frequently asked questions when it comes to crafting a generative AI engineer job description.

What is the salary of a generative AI engineer?

According to ZipRecruiter, the average annual salary for a generative AI engineer in the US – as of February 2024 – is estimated to be $115,864. This can vary based on factors like location, company, and tasks involved.

What does a generative AI engineer do in a day?

While their specific tasks may differ, generative AI engineers review data, refine algorithms, and train models to improve accuracy. They might attend meetings with cross-functional teams to align teams on project goals and troubleshoot issues.

They also dedicate time to staying ahead of technological advancements. Coding is a significant part of their day, alongside testing AI models and analyzing their performances.

Finally, they document their findings and progress, ensuring knowledge transfer and the scalability of their solutions.

Hire the best generative AI engineers with TestGorilla

A top generative AI engineer can elevate your AI systems for creating content and find creative solutions for complex technical issues. Crafting a dynamic job description is vital to attracting the best engineers for your company. Your job posting must be detailed, specific, and appealing to experienced engineers.

The next area of focus is accurately evaluating candidates, and TestGorilla's hundreds of tests can help you identify engineers most suited for your role and team.

Curious to learn more about TestGorilla? Book a live demo or sign up for a free account today!