New AI Job Titles in 2025 (That Didn’t Exist 2 Years Ago)

In just the past two years, the AI landscape has transformed. Gone are the days when “Data Scientist” and “ML Engineer” were the only technical titles in town. Today, generative AI and large language models (LLMs) have birthed entirely new AI job titles that didn’t exist in 2022.

Whether you’re hiring your next AI expert or considering a career pivot, these emerging roles offer a glimpse into how companies are building smarter, safer, and more scalable AI products in 2025.

Let’s break down the six AI job titles you need to know.

Prompt Engineer

A prompt engineer specializes in crafting the questions or instructions given to generative AI models to get the best results. In practical terms, they “talk to the AI” in a way that the AI produces sound output.

This may involve determining the optimal phrasing to enable a model to generate accurate answers or creative content. It’s a mix of creativity and technical understanding of how AI models respond to language. 

Core Skills

• Strong Writing Skills: Use concise, clear language and creative phrasing to craft prompts that AI models understand well.
• Curiosity & Experimentation: Constantly testing different approaches and learning from what output each prompt variation produces.
• Analytical Insight: Analyze AI outputs critically, identifying where a prompt might be misunderstood and figuring out how to improve it.
• Understanding AI Behavior: Developed an intuition for how generative models respond to predict what kind of prompt will yield the best result.

Tools

• OpenAI Playground, Claude, Gemini
• LangChain, PromptLayer
• Regex, YAML, Markdown
• Vector databases like Weaviate, Qdrant

Read our blog Advanced Prompt Engineering Strategies!

MLOps Engineer

An MLOps engineer deploys, monitors, and maintains ML models in production, applying DevOps-like practices to machine learning.

In the context of generative AI, these engineers handle the heavier infrastructure needs of large models. They set up continuous integration/continuous deployment (CI/CD) pipelines for ML, manage cloud resources, and ensure models (like GPT-based services) are scaling properly and running reliably. 

If you have a generative model serving thousands of users, the MLOps engineer ensures it remains up, fast, and secure. 

They bridge the gap between the data science team and IT operations, enabling brilliant AI prototypes to become stable, scalable applications.

Core Skills

• DevOps Mindset: Apply software engineering best practices (automation, version control, testing) to ML workflows.
• Scripting & Automation: Strong scripting skills to automate deployments and manage infrastructure as code.
• Problem Resolution: Quickly troubleshoot production issues, whether it’s a failing pipeline or a slow model response, to minimize downtime
• Communication: Collaborate with data scientists, engineers, and IT to translate model needs into system constraints.

Tools

• Docker, Kubernetes
• MLflow, Kubeflow, Metaflow
• Jenkins, GitHub Actions, CircleCI
• Prometheus, Grafana, SageMaker

Read our blog How to Hire a Machine Learning Engineer to learn about the skills an ML Engineer must have

Model Validator

Think of this New AI job title as AI quality assurance. A model validator rigorously tests AI models before and after deployment to ensure they are accurate, fair, and robust.

Before an AI model goes live, model validators run it through extensive validation datasets, catch edge cases, and identify weaknesses. Is the model making mistakes on specific subgroups of data? Is it overfitting or likely to drift over time?

In high-stakes fields like finance or healthcare, this role is critical; you don’t want an unchecked model approving loans it shouldn’t or misdiagnosing patients. 

Model validators often work independently of developers, providing an objective check (similar to a software tester for code) to ensure that an AI model meets the required standards of performance and ethics before it’s released.

Core Skills

• Model Evaluation: Assess models using metrics like accuracy, ROC, AUC, and F1-score.
• Bias & Robustness Testing: Detect and correct unfair patterns or data drift.
• Attention to Detail: Spot edge cases and rare failures that others may miss.
• Documentation: Provide clear reports that stakeholders can understand and trust.

Tools

• pytest, unittest (for ML testing)
• Fairlearn, Bias Toolkit
• Confusion matrix evaluators, AUC/ROC tools
• Data drift detection libraries: Evidently, WhyLabs

Model Manager

As organizations deploy numerous AI models, someone needs to keep track of them. A model manager oversees the lifecycle of AI models from creation to deployment, updates, and eventual retirement. 

They maintain an inventory of models (including which models we have, which version is running, and where they are deployed), monitor their performance over time, and ensure models are reused when appropriate rather than reinvented. 

Core Skills

• Organization: Manage model registries, track versions, and maintain audit logs.
• Monitoring & Alerts: Ensure models remain accurate and are retrained when needed.
• Cross-Team Coordination: Align ML engineers, product owners, and data scientists.
• Governance: Ensure models comply with internal and external policies.

Tools

• MLflow, SageMaker Model Registry
• Neptune.ai, Weights & Biases
• Grafana, DataDog (monitoring)

Notion, Confluence (for documentation)

Synthetic Data Specialist

A synthetic data specialist focuses on generating and using artificial data that simulates real data. 

They create and curate synthetic datasets to train AI models, especially when actual data is scarce or privacy must be protected. 

For instance, if a team needs thousands of medical images to train a vision model but has only a few (and cannot share patient data), a synthetic data specialist might generate realistic images to augment the dataset.

Core Skills

• Data Simulation: Replicate real-world data distributions for training.
• Privacy Compliance: Design data pipelines that protect user privacy.
• Model Training Awareness: Understand how synthetic data affects downstream model performance.
• Quality Assurance: Test synthetic data for realism and statistical integrity.

Tools

• GANs, VAEs, Unity/Unreal Simulators
• YData, Mostly AI, Gretel.ai
• Data augmentation libraries: Albumentations, imgaug
• Privacy-preserving tools: Differential Privacy Libraries

Fine-Tuning/LLM Ops Engineer

An LLM Ops or fine-tuning engineer specializes in adapting pre-trained models to custom needs. 

They prepare domain-specific datasets and fine-tune the model’s parameters so that, for example, a general AI becomes an expert at, say, legal document analysis or customer support chat. 

They also handle the operational side, deploying these fine-tuned models, monitoring them for issues such as “hallucinations” (incorrect facts), and updating them as new data becomes available.

This role combines elements of a model trainer and an operations engineer for large language models. 

Core Skills

• Model Customization: Fine-tune LLMs with custom data for specific use cases.
• Scaling & Efficiency: Deploy and optimize large models across infrastructure.
  Prompt Engineering: Craft and evaluate prompt strategies during finetuning.
• Monitoring & Iteration: Track hallucinations, bias, and output drift in production.

Tools

• Hugging Face Transformers, LoRA, PEFT
• OpenAI Fine-Tuning API, Anthropic Claude Workbench
• Ray Train, Deepspeed, FSDP
• TensorBoard, WandB, LLM monitoring tools

The world of AI is no longer limited to a few core roles.

Companies building AI products today are staffing up with new specialists: Prompt Engineers, Model Validators, MLOps experts, and more. These new AI job titles are redefining what it takes to build trustworthy, scalable, and high-performing AI systems.

Ready to build your AI dream team?

At ClickIT, we help companies staff up fast with top-tier AI talent. Whether you need staff augmentation or full-cycle development, we have the experience and expertise to get your AI initiative off the ground.

Let’s build the right team for your next AI project. 

FAQs about New AI Job Titles