AI Engineer vs. ML Engineer: Who To Hire for Your AI/ML Project

An AI engineer builds systems that apply artificial intelligence to real-world products, while an ML engineer specializes in training and deploying models that learn from data. Both roles are in high demand, but they differ in focus, tools, and project outcomes.

Key takeaways about AI vs. ML engineers

• AI engineers build intelligent products using NLP, computer vision, and generative AI. ML engineers focus on model training, data pipelines, and predictions.
• Your project goal determines the right hire: AI engineers for product builds, ML engineers for predictive modeling.
• Freelance AI engineers on Upwork charge $35-$60/hour; ML engineers charge $50-$200/hour.
• Demand for AI-enabled skills grew 109% year over year, per the 2026 Upwork In-Demand Skills report.

The demand for Artificial Intelligence (AI) and machine learning (ML) engineers is growing fast as companies increasingly look to apply artificial intelligence to real-world problems. According to the 2026 Upwork In-Demand Skills report, demand for AI-enabled skills grew 109% year over year. AI integration (+178%), AI chatbot development (+71%), and AI data annotation (+154%) were among the fastest-growing skills. Machine learning ranked third among the most in-demand data science and analytics skills on the platform.

From automating customer service to optimizing logistics, AI-powered solutions are changing how businesses operate. Getting the right solution depends on having the right type of engineer behind it. AI and ML roles often overlap, but their core skills, responsibilities, and outputs are different.

This guide compares the differences between AI and ML engineers and gives clear direction on who to hire based on your project needs.

Key differences between AI and ML engineers

While AI and ML engineers often collaborate and share overlapping skills, their roles differ in focus, tools, and project outcomes. This table highlights the distinctions that define each role.

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What does an AI vs. ML engineer do?

AI and ML engineers both work with intelligent systems, but they apply different skills to different challenges. This section covers what each role is, their day-to-day responsibilities, and the tools, frameworks, and platforms they use to get work done.

What is an AI engineer?

An AI engineer is a professional who designs, builds, and deploys systems powered by artificial intelligence, including tools for computer vision, natural language processing (NLP), generative AI, and robotics. AI engineers:

• Develop conversational AI tools. Create chatbots, virtual assistants, and voice recognition systems.
• Integrate AI into products. Deploy models into real-world systems such as robotics or automation workflows.
• Optimize human-machine interaction. They build applications that interact with the real world, respond to voice or visual inputs, and automate tasks that once required human judgment. Examples include chatbots in customer service and predictive analytics in finance.

Day-to-day responsibilities of AI engineers

AI engineers balance technical development with cross-functional collaboration. Here's what the typical day-to-day work of an AI engineer includes:

• Design and implement AI models and algorithms
• Preprocess and analyze data for real-world applications
• Collaborate with software developers to integrate AI solutions into user-facing products or internal workflows
• Test, validate, and optimize models for performance and accuracy

Common tools and platforms used by AI engineers

AI engineers rely on a variety of languages, frameworks, and platforms to build, train, and deploy intelligent systems. They often use these tools and platforms:

• Programming languages. Python, Java
• Frameworks. TensorFlow, PyTorch, OpenCV, LangChain, LlamaIndex
• APIs. Microsoft Azure AI Services, Google Cloud AI
• Platforms. Dialogflow, Rasa (for conversational AI)

Freelance AI engineers are especially valuable for prototyping and building niche AI applications, without the overhead of a full-time team.

What is an ML engineer?

An ML engineer is a professional who builds, trains, and deploys machine learning models that process data to automate decisions, forecast outcomes, and detect patterns at scale. Their responsibilities span the full machine learning life cycle — from data preparation to deployment. Here’s what the typical day-to-day work of an ML engineer includes:

• Designing and training models. Build algorithms that learn from data and improve with continued use.
• Preprocessing and structuring data. Clean and format raw datasets to make them usable for modeling.
• Engineering features and selecting algorithms. Create meaningful variables and choose the most effective models for the task.
• Tuning and evaluating models. Optimize hyperparameters and use metrics like accuracy, recall, and AUC to assess performance.
• Deploying and managing ML pipelines. Automate the steps from training to live environments and maintain stability post-launch.
• Monitoring and retraining models. Track real-world performance and adapt models to reflect new data.
• Powering business applications. Support fraud detection, product recommendations, customer churn prediction, and inventory or demand forecasting using statistical modeling and time-series analysis.
• Communicating insights. Translate model outcomes into actionable business intelligence for stakeholders.

Tools and platforms commonly used by ML engineers

Machine learning engineers work with a specialized tech stack to streamline data processing, model training, and deployment. ML engineers tend to use these frameworks, tools and platforms:

• Languages. Python, Java, SQL, Scala
• Frameworks. TensorFlow, PyTorch (for model training and deep learning)
• Libraries. Scikit-learn, XGBoost, LightGBM
• Platforms. Databricks, MLflow, H2O.ai, Weights & Biases
• Cloud ecosystems. AWS SageMaker, Google Cloud Vertex AI

ML engineers are a strong fit for data-intensive projects where predictive accuracy and continuous model improvement are key to success."

Role overlap between ML and AI engineers

Many job postings list the combined role as AI/ML engineer, reflecting this overlap. Both roles share core engineering skills in AI programming languages — like Python and Java — data science, and algorithm development. Whether building AI agents or fine-tuning ML models and neural networks, engineers in both fields need to understand data pipelines, model training, and performance metrics.

ML engineer vs. AI engineer salary and rates

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Sources: Salary.com (2026), Upwork hourly rates

Both AI and ML engineers are AI careers with high demand, which is reflected in their compensation. Established companies often offer six-figure salaries or more when hiring for AI roles with these skill sets, especially those located in tech hubs. According to Salary.com, AI engineers earn a median salary of $113,349 per year in the U.S. ML engineers earn a similar median of $109,929 per year. Top-paying locations include California, Massachusetts, and Washington, D.C.

Other factors that might affect AI and ML engineer salaries include:

• Experience. Seniority and a proven track record can significantly increase rates.
• Specialization. Niche areas like deep learning or reinforcement learning might command higher rates.
• Location. Professionals in cities like San Francisco or New York often charge more due to the cost of living and concentration of tech companies.
• Education. A master's degree, advanced degrees, or certifications can also affect pricing.

Freelance ML engineers on Upwork typically charge $50-$200 per hour. AI engineers charge $35-$60 per hour. Freelance rates vary based on specialization, experience, and project scope.

The job outlook for these roles is strong. While the Bureau of Labor Statistics doesn't track AI or ML engineers as standalone categories, the closest occupations are projected to grow 15% (software developers) and 20% (computer and information scientists) from 2024 to 2034. That's far above the 3% average for all occupations, reflecting growing business investment in AI and automation.

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How to choose between hiring an  AI engineer vs. ML engineer

Choosing between hiring an ML engineer vs. AI engineer depends on the specific outcome you need. If your priority is shipping an AI-powered product, an AI engineer is the right fit. If you need to improve model accuracy or scale a data pipeline, an ML engineer is the better choice.

While some projects may benefit from both skill sets, understanding where each role excels can help you hire more efficiently.

Hire an AI engineer vs. ML engineer when you need to:

• Build NLP-powered chatbots or voice assistants
• Develop computer vision applications (e.g., facial recognition, object detection)
• Integrate generative AI tools for content creation or automation
• Program robots or autonomous systems that interact with the physical world
• Deploy AI agents that assist with decision-making and task execution in real time

Hire an ML engineer vs. AI engineer when you need to:

• Develop predictive models using historical or real-time data
• Build classification systems (e.g., spam detection, medical image analysis)
• Perform feature engineering and tune machine learning pipelines
• Analyze time-series data for forecasting (e.g., sales, demand, or stock prices)
• Automate decision-making based on large-scale datasets

Do you need both types of engineers?

Some projects, like fraud detection systems or AI-driven personalization engines, require both machine learning and intelligent system design. In these cases, consider:

• Hiring people with AI/ML hybrid experience
• Engaging multiple specialists for different phases (e.g., data prep, modeling, system integration)

Scoping your project accurately from the start helps you match the right expertise to your business goals.

Skills and education to look for in AI and ML engineers

Whether you're hiring an AI engineer or an ML engineer, both roles require strong foundations and prerequisites in math, programming, and data science, with some differences in focus.

Core technical skills

AI and ML engineers share a foundation in programming, statistics, and data structures, but their technical expertise diverges based on their focus area.

AI engineers specialize in technologies that power intelligent systems designed to supplement or increase a person's output. Their core skills include:

• Deep learning frameworks. Proficiency with TensorFlow, PyTorch, or Keras.
• Neural network architectures. Knowledge of CNNs, RNNs, transformers, and attention mechanisms.
• Natural language processing (NLP). Experience with tokenization, embeddings, and fine-tuning large language models.
• Computer vision. Familiarity with image and video processing techniques.
• Generative AI tools. Understanding of diffusion models, prompt engineering, and LLM-based applications.

ML engineers focus on the infrastructure and algorithms that make data-driven modeling possible. Their core skills include:

• Programming and data handling. Strong in Python, SQL, and libraries like Pandas, NumPy, and Scikit-learn.
• Statistical modeling and machine learning algorithms. Deep understanding of regression, classification, and ensemble methods.
• Feature engineering and model optimization. Expertise in creating high-quality inputs and tuning hyperparameters.
• Version control and reproducibility. Experience with Git, MLflow, or DVC for experiment tracking.
• Pipeline and workflow tools. Proficiency with TensorFlow Extended (TFX), Airflow, or Kubeflow for scalable ML systems.

Popular certifications for ML and AI engineers

Certifications can help validate technical expertise and provide structure for continued learning. They also help hiring managers understand how up-to-date a candidate's knowledge is. Here are some of the most relevant AI certifications or machine learning certifications to look for:

• IBM AI Developer Professional Certificate. Covers AI fundamentals, NLP, and building with Watson. Useful for validating a candidate's foundational AI knowledge
• Microsoft Azure AI Engineer Associate. Ideal for deploying AI solutions in enterprise settings
• Google Cloud Professional ML Engineer. Covers designing, building, and deploying ML and generative AI solutions in the GCP ecosystem, including Vertex AI
• AWS Certified Machine Learning Engineer - Associate. Validates skills in building, deploying, and maintaining ML workloads on AWS, including model training, pipeline automation, and monitoring

Tips for hiring AI and ML engineers

Hiring the right AI or ML engineer for a project starts with clearly defining the scope and knowing what skills to prioritize. These projects often move quickly, so finding someone who can contribute without a long onboarding period is important. These tips can help you find the right ML or AI engineer candidate::

• Define the project scope clearly. Identify whether you need full AI system integration, a focused model for tasks like recommendation systems, or specific machine learning development to narrow your search.
• Prioritize relevant technical skills. Look for hands-on experience with frameworks like TensorFlow, PyTorch, or Scikit-learn, depending on your project's needs.
• Assess their practical problem-solving ability. Choose candidates who can apply theory to real-world challenges, not just discuss algorithms. The right interview questions for ML and AI engineers can help assess this alongside the work examples they shared.
• Evaluate their cross-functional communication. Strong collaboration with data scientists, product teams, and stakeholders ensures smoother project execution.
• Seek adaptability and speed. Opt for engineers who can contribute quickly and adjust to changing goals or technologies without long onboarding periods.

Most relevant skills for short-term contracts

When hiring for fast-moving or short-term AI/ML contracts, focus on practical, deployment-ready skills that allow the engineer to contribute quickly. Prioritize these skills:

• Experience using prebuilt frameworks (like TensorFlow, Hugging Face, Scikit-learn)
• A deep understanding of Python and data processing tools (e.g., Pandas, NumPy)
• Deployment experience with platforms like AWS, GCP, or Azure
• Familiarity with tools like MLflow or Docker for reproducibility

Look for specialization aligned with your use case, such as NLP, time-series forecasting, classification, or deep learning.

Tips for evaluating ML and AI engineering candidates

When reviewing potential hires for AI or ML roles, knowing what signals to prioritize can help you filter top-tier talent more efficiently. These indicators can help you find engineers who are continuously learning, curious, and the right fit for your role:

• A strong portfolio. Look for relevant real-world projects, especially anything related to your industry or problem type.
• Recent GitHub activity. Open-source contributions, sample notebooks, or well-documented repositories can show how someone approaches problem-solving.
• Appropriate certifications. While not required, credentials from Google Cloud, AWS, Microsoft, or IBM signal structured training and up-to-date knowledge.
• Proposal clarity. A good job candidate will ask the right questions, clarify scope quickly, and propose a thoughtful development approach.

Hire AI and ML engineers on Upwork

AI and ML engineering skills are shaping industries from healthcare to finance, and demand for these roles continues to grow.Both engineering roles give you access to professionals with a wide range of backgrounds, from data science to software engineering. This means you can find candidates with the right mix of skills for your specific project without limiting your search to a single discipline.

Ready to bring AI into your workflow? Hire top-rated AI engineers or ML experts on Upwork today and find the perfect match for your next big project.

FAQs about an AI engineer vs. ML engineer

Understanding the difference between AI and ML engineers can help you make better hiring decisions. These are some of the most common questions clients ask when evaluating AI and ML engineering talent.

Is an AI engineer the same as a machine learning engineer?

An AI engineer is not the same as a machine learning engineer, though the roles share overlapping skills. AI engineers work across a broader range of technologies, including NLP, computer vision, robotics, and generative AI. ML engineers specialize in building, training, and deploying models that learn from data. The day-to-day focus and technical output differ, but many projects benefit from both skill sets.

What are the key differences between hiring freelance AI/ML engineers and building an in-house team?

The key differences between hiring freelance AI/ML engineers and building an in-house team come down to flexibility, speed, and cost. Freelance engineers offer faster onboarding, global talent access, and lower overhead, making them ideal for short-term or exploratory projects. In-house teams are a better fit for long-term initiatives that need deep domain knowledge. Many businesses combine both approaches.

How much does it cost to hire an AI or ML engineer?

Hiring an AI or ML engineer costs vary based on engagement type, experience, and specialization. On Upwork, freelance AI engineers typically charge $35-$60 per hour, while ML engineers charge $50-$200 per hour. Full-time U.S. salaries average around $113,000 for AI engineers and $110,000 for ML engineers, according to Salary.com (2026). Rates also depend on project complexity and location.

What skills should I look for when hiring an AI/ML engineer?

The skills to prioritize when hiring an AI or ML engineer depend on your project goals. For AI engineers, look for experience with deep learning frameworks like TensorFlow and PyTorch, along with NLP and computer vision. For ML engineers, prioritize strong Python skills, data pipeline experience with tools like Scikit-learn and MLflow, and cloud deployment on AWS or GCP. For both roles, logical problem-solving and clear communication are strong signals.

Can one person handle both AI and ML engineering work?

Yes, one person can handle both AI and ML engineering work, especially on mid-scale projects. For example, a professional with hybrid skills could build a recommendation engine that requires both model training and product integration. Similarly, a chatbot project often involves NLP model work alongside system architecture.

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The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

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