Artificial intelligence is now everywhere, from everyday apps to complex enterprise systems. New startups emerge, established companies expand AI-related jobs, and demand for skilled professionals continues to grow. That’s why now is a great opportunity to seek AI jobs and perhaps even start a new career, as the market offers plenty of opportunities for both experienced professionals and beginners.
We browsed through the best AI job positions and compiled a list of the most popular positions you can apply for.
1. Machine Learning Engineer (ML Engineer)
ML engineers remain a core role in any modern AI team. This AI career path involves designing and deploying machine learning systems that power real products. Machine learning engineers rely on a mature AI stack that blends algorithms with engineering infrastructure. This includes Python-based ML frameworks, cloud platforms, and tools for model deployment, monitoring, and automation.
Key hard skills and tools for this AI job include:
- Python and SQL
- Machine learning algorithms and statistics
- Deep learning frameworks (PyTorch, TensorFlow)
- Data processing libraries (pandas, NumPy)
- Model deployment and APIs
- Docker and containerization
- Cloud platforms (AWS, GCP, Azure)
- Version control (Git)
- CI/CD and basic MLOps practices
We see the demand for machine learning engineers grow as AI becomes embedded across industries. In the United States, this role ranks among the highest-paying AI jobs. According to Indeed, the average base salary is $182,904 per year, with reported ranges from $109,699 at the low end to $304,961 at the high end.
With strong market demand and clear business impact, this role remains one of the most reliable answers to how to make money with AI.
2. Data Scientist (Applied AI / Product DS)
We consider the Data Scientist role a bridge between raw data and decision-making powered by artificial intelligence. Data scientists analyze large datasets and build statistical or machine learning models that answer concrete business questions. Unlike ML engineers, their focus is less on infrastructure and more on experimentation and turning data into insights that guide strategy and product direction.
Key hard skills and tools include:
- Python and SQL for data analysis
- Statistics, probability, and hypothesis testing
- Machine learning fundamentals
- Data manipulation: pandas, NumPy
- Modeling tools: scikit-learn, statsmodels
- Visualization: Matplotlib, Seaborn, Plotly
- BI tools: Tableau, Power BI, Looker
- Jupyter Notebooks and data storytelling techniques
We see strong hiring for this role across technology, finance, healthcare, retail, and media. The U.S. Bureau of Labor Statistics projects employment of data scientists to grow 34% from 2024 to 2034, much faster than average for all occupations. In terms of compensation, salaries vary by experience, industry, and location. The average data scientist salary in the United States is around $129,505 per year, with reported ranges between approximately $80,663 and $207,921.
3. AI Product Manager (AI PM)
An AI Product Manager defines how artificial intelligence fits into a product. This role connects business goals with technical constraints and user expectations. The work involves problem framing, success metrics, and decision-making around model behavior, as seen in consumer-facing AI applications such as Clever Cleaner: AI Cleaner App, SmartCleaner, and AI Cleaner, where AI helps users clean out duplicate and nearly identical photos. In that context, AI translates directly into measurable user value. AI PMs don’t build or train these models, but they decide why a model exists, what problem it should solve.
Key hard skills and tools include:
- Product strategy and roadmap planning
- AI and ML literacy at a conceptual level
- Metric definition for AI features
- User research for AI-driven interfaces
- Analytics platforms
- Product management tools
- Clear technical communication
Average U.S. salaries are between $145,000 and $175,000 per year, with senior roles exceeding $190,000 in technology companies.
4. AI QA / Model Evaluation Analyst
An AI QA or Model Evaluation Analyst examines how machine learning models behave after release in real conditions. The role focuses on output checks, failure discovery, quality measurement, and consistency tracking against set criteria, which are defined before launch. The work stays close to user impact and live data streams, where small deviations matter. When a model drifts or hallucinates, or when performance degrades slowly, AI QAs are the ones who spot the issues early through careful comparison and domain insight.
Key hard skills and tools include:
- Machine learning fundamentals
- Model evaluation metrics with structured error review
- Python and SQL for analysis workflows
- AI test case design for production systems
- LLM quality assessment methods
- Prompt tests with regression coverage
- Data labeling tools with quality controls
- Model monitoring platforms
- Experiment tracking systems
Glassdoor reports average U.S. salaries between $115,000 and $150,000 per year, which depends on scope and industry. Senior positions with a strong focus on LLM evaluation often exceed $170,000, especially in sectors where mistakes cost real money. We think this role grows quietly and stays relevant even when trends shift.
5. Deep Learning AI Engineer (Computer Vision / NLP)
Next is Deep Learning Engineer, a highly specialized AI professional who focuses on building and optimizing neural-network-based systems for complex tasks, such as computer vision and natural language processing (NLP). This role is more complex and requires more skill than classical ML and works with deep neural architectures that learn directly from images, video, text, or audio.
Key hard skills and tools include:
- Python and advanced deep learning concepts
- Neural network architectures (CNNs, RNNs, Transformers, Vision Transformers)
- Frameworks: PyTorch, TensorFlow, Keras
- NLP tools: Hugging Face Transformers, spaCy
- Computer vision tools: OpenCV, YOLO, Detectron2
- GPU computing basics (CUDA concepts)
- Model optimization, fine-tuning, and evaluation
- Experiment tracking (Weights & Biases, TensorBoard)
Market demand for deep learning engineers has surged with the rise of generative AI and AI-powered vision systems. The World Economic Forum’s Future of Jobs Report identifies AI and machine learning specialists as among the fastest-growing roles globally, with projected net growth exceeding 80% by 2030. Data reflects this high demand and specialization. Deep learning engineers in the United States earn an average total compensation of approximately $170,000–$190,000 per year.
6. AI Research Scientist (R&D)
An AI Research Scientist works on problems where existing methods stop working well. The role focuses on new model designs, training strategies, and evaluation approaches that push performance beyond current limits. Work often happens before clear product requirements exist. Some ideas ship. Many do not. Research teams expect exploration, failed experiments, and slow progress before visible results appear.
Key hard skills and tools include:
- Advanced machine learning and deep learning theory
- Strong mathematical background (linear algebra, probability, optimization)
- Python and research-grade ML frameworks (PyTorch, TensorFlow, JAX)
- Transformer architectures & diffusion models
- Large-scale experimentation and model evaluation
- Research tooling: Jupyter, Weights & Biases, MLflow
- Scientific writing and peer-review publication experience
- Familiarity with distributed training and GPU/TPU systems
Glassdoor reports average U.S. compensation for AI research scientists around $180,000 per year, with senior roles often exceeding $230,000, with higher pay tied to publication history and model ownership.
7. MLOps Engineer (AI Platform / Model Operations)
An MLOps Engineer focuses on the operational side of machine learning. This role supports AI teams by building systems that allow models to move smoothly from experimentation to production and stay reliable after deployment. The work centers on automation and monitoring rather than model design itself. MLOps engineers work mainly in cloud-based environments. They deploy models through containerized services and build automated pipelines that handle training runs and post-deployment monitoring.
Key hard skills and tools include:
- Python and shell scripting
- Linux systems and networking basics
- Docker and Kubernetes
- CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
- MLOps platforms: MLflow, Kubeflow, SageMaker, Vertex AI
- Workflow orchestration: Airflow, Prefect
- Infrastructure as Code: Terraform
- Monitoring and observability: Prometheus, Grafana
- Model versioning, data lineage, and drift detection
According to Glassdoor, MLOps Engineers in the United States earn an average total compensation of approximately $165,000 per year, with senior roles frequently exceeding $190,000–$210,000.
8. Data Engineer (AI data pipelines)
A Data Engineer builds the data backbone that AI systems depend on. This role prepares raw information for machine learning use and keeps data reliable once products reach scale. The work focuses on ingestion, transformation, validation, and delivery of data to downstream AI teams. When data breaks, models follow. When pipelines stay clean, everything upstream behaves better.
Key hard skills and tools include:
- SQL and Python
- Data modeling for analytics and ML
- ETL and ELT pipeline design
- Apache Spark or similar processing frameworks
- Airflow or Prefect for orchestration
- Cloud data warehouses
- Object storage systems
- Data quality validation tools
According to the U.S. Bureau of Labor Statistics, data-related roles show growth well above the average through 2034, driven by AI and analytics adoption. Salary data reflects this demand. Indeed reports an average Data Engineer salary of about $145,000 per year in the United States, with senior roles often exceeding $180,000 in large technology companies.
9. LLM / Generative AI Engineer (apps + agents)
An LLM / Gen AI Engineer builds user-facing systems that rely on large language models. The role focuses on application logic, agent behavior, and controlled interaction with external tools or data sources. Work often includes chat interfaces, task-oriented agents, retrieval-based systems, and workflow automation. The goal stays practical: make models useful and keep outputs predictable enough for real users.
Key hard skills and tools include:
- Python and JavaScript
- LLM APIs and open-source models
- Prompt design with evaluation methods
- RAG system design
- Vector databases
- Agent frameworks
- API design and backend services
- Basic ML quality metrics
Glassdoor reports average total compensation for LLM and generative AI engineers between $160,000 and $190,000 per year in the United States. Senior roles tied to agent-based systems often exceed $210,000, especially in AI-first startups.
10. AI Security / Privacy engineer
An AI Security or Privacy Engineer focuses on risk control in AI systems. This role protects models, data flows, and user interactions from misuse, leakage, or manipulation. Work often centers on threat analysis for AI features, access control design, and compliance with privacy rules. When AI systems handle sensitive data or operate at scale, this role becomes mandatory rather than optional.
Key hard skills and tools include:
- Security fundamentals for software systems
- Machine learning system architecture
- Data privacy concepts and compliance standards
- Identity and access management systems
- Secure API design
- Threat modeling for AI use cases
- Logging and audit frameworks
- Secure cloud configurations
Demand grows as AI systems become more widely used in finance, healthcare, insurance, and government sectors. Companies often add this role after security reviews flag AI-specific risks. Although this role rarely trends publicly, hiring pressure stays steady.
Closing note
As we can see across these roles, the corresponding uses of artificial intelligence mentioned already answer the broader question of ‘what is artificial intelligence in practice?’ These show how AI actively shapes products, security, research, data infrastructure, and everyday business decisions.
Each career path reflects a different way AI creates value. Demand grows because companies now treat AI as a long-term capability rather than an experiment.
We hope this list will help you discover more AI tech jobs that you can explore. Choose the position that matches your experience and skillset to become truly successful in this growing industry.