Artificial intelligence engineers build the machine learning models, data pipelines, and generative AI applications that help businesses automate operations, forecast outcomes, and gain competitive advantage. From computer vision in manufacturing to natural language processing in customer support, skilled AI engineers turn raw data into intelligent systems that drive measurable results.
What does an artificial intelligence engineer do?
An artificial intelligence engineer designs, builds, and deploys AI-powered systems that solve specific business problems. The role spans the full life cycle of an AI project, from collecting and preparing data to training models and putting them into production environments where they deliver value every day.
AI engineers often do the following tasks:
- Build and train machine learning models for tasks like classification, prediction, anomaly detection, and recommendation
- Integrate AI capabilities into existing business applications, APIs, and workflows
- Design and maintain data pipelines that collect, clean, and transform raw data into formats suitable for model training
- Optimize AI system performance by tuning hyperparameters, reducing latency, and improving accuracy over time
- Develop generative AI applications, including large language model (LLM) fine-tuning, retrieval-augmented generation (RAG) systems, and prompt engineering solutions
How to hire an artificial intelligence engineer on Upwork
Upwork gives you access to AI engineers with experience across machine learning, natural language processing, computer vision, and generative AI. Follow these four steps to find and hire the right professional for your project.
Step 1: Post a job
Start by specifying which AI specialization your project requires, whether that's ML model development, NLP, computer vision, or generative AI. Name the frameworks and cloud platforms your team uses so candidates can confirm their experience.
- Define your project scope, timeline, and expected deliverables for the AI system
- List required specializations such as deep learning, reinforcement learning, or transformer architectures
- Identify cloud platforms (AWS SageMaker, Google Cloud AI Platform, Azure ML) and frameworks (TensorFlow, PyTorch, scikit-learn) relevant to your stack
- Specify whether you'll provide training data or expect the engineer to source and prepare it
- Indicate whether the project involves building a custom model, fine-tuning an existing model, or integrating AI APIs
- Define any latency, accuracy, or cost targets the solution should meet
- Share your expected budget and timeline
- Reference this artificial intelligence engineer job description template for guidance on structuring your requirements
Use the Job Post Generator — powered by Uma™, Upwork's Mindful AI — to speed things up. Describe your AI project needs in a few sentences, and Uma will draft a detailed job post for AI engineers that you can review and customize.
Step 2: Evaluate candidates
Focus on evidence of real-world AI engineering work. Candidates who've deployed models into production environments bring different skills than those who've only worked on research prototypes.
- Review portfolios for deployed AI projects, GitHub repositories with ML code, and published research or technical writing on AI topics
- Evaluate proficiency in relevant frameworks (TensorFlow, PyTorch, Hugging Face) and cloud deployment experience (AWS, GCP, Azure)
- Look for experience deploying AI models to production, not just building prototypes
- Review examples of LLM, computer vision, NLP, or predictive modeling projects similar to yours
- Confirm familiarity with vector databases, model serving, or inference optimization, if relevant
Use Uma's Best Match insights to generate candidate shortlists with side-by-side comparisons of AI engineers' skills and experience.
Step 3: Interview your top choices
Interview top candidates to check both their technical capabilities and communication skills.
- Ask about their approach to data preparation, feature engineering, and handling imbalanced or noisy datasets
- Discuss model training workflows, algorithm selection criteria, and how they validate model performance
- Explore their MLOps experience, including CI/CD for ML pipelines, model monitoring, and production deployment strategies
- Present a sample problem relevant to your project and ask them to walk through their solution approach
- Ask how they evaluate model performance and monitor it after deployment
- Discuss their approach to managing hallucinations, bias, or model drift, when applicable
- Explore how they balance accuracy, inference speed, and infrastructure costs
- Review these artificial intelligence engineer interview questions for additional guidance
Schedule and conduct interviews within Upwork Messages. You'll get an immediate transcript and summary of each conversation, so you can compare candidates without taking detailed notes.
Step 4: Agree on scope and begin work
Choose between fixed-price contracts for well-defined AI deliverables and hourly contracts for ongoing model development or research work.
- Define how model performance will be measured and accepted before project completion
- Clarify ownership of datasets, trained models, prompts, and source code
- Establish a plan for model monitoring, retraining, or ongoing optimization after deployment
- Break your AI project into milestones: data collection and preparation, model training, evaluation and testing, and production deployment
Use Upwork's contract workroom and messaging to share datasets, model specifications, and progress updates. Take advantage of identity verification, payment protection, hourly tracking, and project funds for financial security on every contract.
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.
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.