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It used to be that “AI” in a job title felt like science fiction. Now it’s hearing about “AI job titles” is just a normal Tuesday.
If you’re in talent acquisition, you’re probably starting to see job reqs float across your desk with unfamiliar titles like Prompt Engineer, LLM Architect, and Responsible AI Lead. These aren’t trends. They’re the new table stakes.
The AI Job Title Boom: What’s Going On?
I pulled together a guide at Ongig because our clients kept asking the same thing: What the heck do these AI job titles even mean?
Turns out, there’s a lot more than just “AI Engineer.” There’s a whole ecosystem of roles, from the folks who build the models, to those who manage them, to the ones making sure they don’t accidentally go rogue (looking at you, AI Ethics Officer).
30+ AI Job Titles Worth Knowing
Here’s the list of AI job titles we give TA teams when they’re trying to write, source, or edit a job description:
Core Technical AI Roles
- AI Engineer – Builds smart systems that can learn, predict, and automate tasks
- Machine Learning Engineer – Focuses on designing and improving learning algorithms
- Deep Learning Engineer – A specialist in neural networks (used in image, speech, and big-data applications)
- Data Scientist – Gathers and analyzes data to guide business and AI model decisions
- NLP Engineer – Works on systems that understand and generate human language
- Computer Vision Engineer – Focuses on teaching machines to interpret images or video
- MLOps Engineer – Maintains and scales machine learning systems after they’re deployed
Emerging AI & Hybrid Roles
- Prompt Engineer – Writes and tests prompts to guide AI tools like ChatGPT to deliver useful outputs
- AI Trainer – Teaches AI models by labeling data and correcting output
- LLM Architect – Designs large language model systems, from structure to scaling
- AI Product Manager – Builds product plans around AI tools and ensures value delivery
- AI UX Designer – Focuses on how users interact with AI tools (chatbots, voice tools, etc.)
- AI Content Strategist – Uses AI to write and optimize marketing and brand content
- AI Innovation Manager – Leads internal efforts to identify and test AI-driven improvements
AI Governance & Ethics Roles
- AI Policy Analyst – Studies and recommends company policies for safe AI use
- AI Ethics Officer – Ensures AI systems are fair, legal, and non-biased
- Responsible AI Lead – Builds frameworks and standards for ethical AI use
Creative AI + Communication Roles
- AI Content Creator – Creates visual, written, or audio content using AI tools
- AI Art Director – Oversees creative projects using generative tools like Midjourney
- Generative AI Designer – Uses tools like DALL·E or Adobe Firefly to generate images and assets
AI Support & Implementation
- AI Solutions Architect – Plans and builds custom AI infrastructure for clients or teams
- AI Project Manager – Coordinates deadlines, teams, and implementation timelines for AI initiatives
- AI Technical Recruiter – Sources and hires top AI and ML talent
- AI Business Analyst – Connects business problems to AI solutions
- AI Chatbot Developer – Builds bots that talk to customers or employees using AI
AI Executive Roles
- Chief AI Officer (CAIO) – Leads overall AI strategy and implementation
- Director of AI – Manages AI teams and ensures their work aligns with company goals
- VP of AI/ML – Oversees high-level technical strategy and staff
You can download our AI Job Title Guide. And we will keep updating it as this stuff evolves.
Which AI Job Titles Actually Attract Candidates?
Some of the most clicked job titles right now:
- Prompt Engineer – still trendy, still mysterious
- AI Product Manager – great for business-minded tech folks
- AI Content Creator – perfect for creatives trying out generative tools
- Chief AI Officer – bold and buzzy for leadership searches
Lesson: Don’t get too cute. Clear > clever. “Machine Learning Engineer” gets more traction than “AI Ninja” every time.
Real Talk: Writing AI Job Titles Isn’t Just Semantics
We’ve worked with dozens of companies trying to scale AI hiring. The job title is a headline. It’s SEO, it’s your first impression, and it sets expectations.
For example, if you swap out “AI Software Developer” for “Machine Learning Engineer,” you might see a 2x increase in qualified applicants. That’s the power of alignment.
Quick Tips for Writing AI Job Titles
- Use plain language: Avoid jargon like “intelligent automation architect.” Say what it is.
- Add relevant keywords: Think about what people search for on LinkedIn or Google.
- Be role-specific: Differentiate between “Data Scientist” and “Prompt Engineer” – they are not interchangeable.
Why I Wrote This
I wrote this because almost every TA team we talk to is being asked to “hire AI talent,” but often without a map. This post gives you a starting point.
If you’re overwhelmed by how fast AI roles are popping up (and changing), we can help. Ongig’s software makes job descriptions easier to manage and update. Whether you’re trying to write a better Prompt Engineer listing or rework your whole tech org chart, request a demo, and we’ll show you how we help.
FAQs
What is the difference between an AI Engineer and a Machine Learning Engineer?
AI Engineers focus on the full system, while Machine Learning Engineers dive deep into algorithms and models specifically.
Why is a Prompt Engineer important?
They write the inputs that get the best outputs from tools like ChatGPT. Without them, your AI might act weird—or useless.
Do I need a Chief AI Officer?
If AI is core to your company strategy, yes. It’s like having a CTO but focused on machine learning and automation.
Where do these roles fit in an org chart?
It depends on your maturity with AI. Startups might combine roles. Enterprises usually separate tech, product, and governance tracks.
What job titles are best for AI content creation?
Try “AI Content Creator,” “Generative AI Designer,” or “AI Content Strategist” depending on the scope of the work.
