AI/ML Engineer Roles Explained: Who To Hire for Your Project

The demand for AI and ML engineers is growing fast as companies increasingly look to apply artificial intelligence to real-world problems. From automating customer service to optimizing logistics, AI-powered solutions are transforming how businesses operate.

But not every project needs the same kind of engineer. Artificial intelligence (AI) and machine learning (ML) roles often overlap, but their core skills, responsibilities, and outputs are different. Knowing the distinction can help you avoid mismatches and achieve better results more quickly.

This guide breaks down 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. The following table highlights the distinctions that define each role.

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What do AI and ML engineers do?

AI and ML engineers both work with intelligent systems, but they solve different types of problems.

AI engineers

Artificial intelligence engineers work in areas like computer vision, natural language processing (NLP), and robotics. These professionals:

• 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 they typically do from day to day:

• 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.
• 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.

ML engineers

ML professionals build models that process data, helping businesses automate decision-making, forecast outcomes, and detect patterns at scale. Their responsibilities span the full machine learning lifecycle — from data preparation to deployment:

• 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 models for performance. Optimize hyperparameters to boost accuracy, efficiency, and reliability.
• Deploying end-to-end ML pipelines. Automate the steps from training to deployment in scalable workflows.
• Monitoring and retraining models. Track real-world performance and adapt models to reflect new data.
• Detecting fraud. Identify suspicious activity in financial services, e-commerce, and other sensitive domains.
• Powering personalization. Support recommendation engines for content, products, and user experiences.
• Predicting customer churn. Flag early signs of customer loss to support retention efforts.
• Forecasting business metrics. Estimate future demand, revenue, or inventory needs using time-series analysis.
• Evaluating model effectiveness. Use metrics like accuracy, recall, and AUC to assess results.
• Communicating insights. Translate model outcomes into actionable business intelligence for stakeholders.
• Managing production deployment. Integrate machine learning solutions into live environments and ensure stability post-launch.

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 tools and platforms:

• Languages. Python, Java, SQL.
• Libraries. Scikit-learn, XGBoost, LightGBM.
• Platforms. Databricks, MLflow, H2O.ai.
• Cloud ecosystems. AWS SageMaker, Google Cloud Vertex AI.

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

Role 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.

Which to hire: An AI engineer or an ML engineer

Choosing between an AI and an ML engineer depends on the specific outcome you're aiming for. While some projects may benefit from both skill sets, understanding where each role excels can help you hire more efficiently.

Hire an AI 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 mimic human judgment in real time

Hire an ML engineer when you need to:

• Develop predictive models using historical or real-time data
• Build classification systems (e.g., spam detection, medical diagnosis)
• 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?

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)

Use these guidelines to scope your project accurately and match the right expertise to your business goals.‍

Skills and education to look for

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

Certifications can help validate technical expertise and provide structure for continued learning. Some of the most relevant credentials for AI and ML engineers include:

• IBM AI Developer Professional Certificate. Strong in AI fundamentals, NLP, and building with Watson.
• Microsoft Azure AI Engineer Associate. Ideal for deploying AI solutions in enterprise settings.
• Google Cloud Professional ML Engineer. Focused on model design and deployment in the GCP ecosystem.
• AWS Certified Machine Learning — Specialty. Amazon's course for cloud-based ML modeling, automation, and tuning.

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 deliver results without much ramp-up is key. Use these tips to find the right 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.
• 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 projects or short-term AI/ML contracts, you'll want to focus on practical, deployment-ready expertise that minimizes ramp-up time and maximizes immediate impact. For fast-moving projects, prioritize:

• 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 candidates

When reviewing potential hires for AI or ML roles, knowing what signals to prioritize can help you filter top-tier talent more efficiently. Look for these signals:

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• 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.

Expected costs for hiring AI and ML engineers

Both AI and ML engineers are in high demand, and their compensation reflects it. On Upwork, machine learning engineers typically charge between $50–$200 per hour. Similarly, AI engineers charge from $35–$60 per hour.

Established companies can offer six-figure salaries or more, especially those located in tech hubs. Other factors that might affect AI and ML engineer salaries include:

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

Hire AI and ML engineers on Upwork

In our rapidly evolving, tech-driven world, the usefulness of artificial intelligence and machine learning engineering is significant, and it's likely to be for some time. These domains are driving advancements in industries across the spectrum, from health care to finance, reflecting the pivotal role these technologies play in shaping our future.

Both roles also offer competitive pay, provide the flexibility to transition between specializations, and offer entry points from a variety of related disciplines. 

Ready to bring AI into your workflow? Hire top-rated artificial intelligence engineers or machine learning experts on Upwork today and find the perfect match for your next big idea.

FAQs

AI and ML engineers are crucial to the development of modern technology, driving innovations that impact nearly every industry. Here are some common questions related to hiring AI and ML engineers.

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

Hiring freelance AI or ML engineers offers flexibility, faster onboarding, and access to a global talent pool, which is ideal for short-term or exploratory projects. In contrast, building an in-house team can be beneficial for long-term initiatives requiring deep domain knowledge, ongoing maintenance, close collaboration with internal stakeholders, and institutional knowledge. 

Businesses should weigh project scope, budget, and strategic goals when deciding between the two approaches. Often, freelance AI and ML engineers are brought in on a short-term basis for their niche skills, while an in-house team focuses on maintenance and continuity.

Can a professional with a background in cybersecurity transition to AI or ML engineering?

Yes, professionals with a cybersecurity background can transition to AI or ML engineering, especially if they have experience with data analysis, algorithm development, and problem-solving. These are popular entry points, and there are many learning paths available. LinkedIn Learning offers some great foundational courses to get started.

Are there bootcamps or courses recommended for someone looking to start in AI or ML engineering?

Yes, several bootcamps and courses are available online, such as those offered by Udacity, Coursera, and others. These programs cover the fundamentals of AI and ML, providing a solid foundation for beginners and professionals looking to upskill.

Adopt AI in your workflow today. Some of the top artificial intelligence engineers and machine learning experts find their clients on Upwork. If you're looking to land your next job in AI or ML, Upwork has you covered there, too. Sign up today to get started. 

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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.

‍Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.