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Lana Ilic
Fullstack Developer
Previously at
Previously at
Previously at
Experience our in-house-like model and hire proven AI Developer in just 4 steps!
Share the roles, skills, experience and engagement type you're looking for. We’ll use this to match you with talent who truly fit your needs.
We handpick top candidates who are pre-vetted for technical skills, remote readiness, and who meet the requirements you set.
Review candidate profiles, ask questions, or schedule interviews. You stay in control of the final decision.
Once you’re ready, kick off the collaboration. With our Managed Hire full-time model, you get a 30-day risk-free trial.
We’ll match you with vetted AI Developer that suits your requirements. It typically takes 2 working days, allowing you to focus on growth instead of hiring.
Our rigorous vetting process combines Advanced AI assessments with expert human evaluation to ensure only top talent makes it through.
Our 30-day trial lets you work with experts before a longer-term commitment. Plus, cancel or replace the talent anytime without fee.
We handle everything from contracts and compliance to payments and performance tracking, allowing you to focus on your project.
AI developers design systems that learn from data, automate decision-making, and enhance digital products with intelligence. From chatbots to recommendation engines, AI is driving value across industries. Hiring AI developers helps you stay competitive by integrating machine learning, computer vision, and NLP into your business operations.
AI has a wide range of applications, and skilled developers can tailor solutions to your business needs. The most impactful use cases include:
Recommendation engines for e-commerce and media
Predictive analytics in finance, sales, and logistics
NLP tasks like chatbots, summarization, and sentiment analysis
Computer vision for object detection, OCR, and facial recognition
AI copilots and generative tools using large language models
Companies that invest in AI developers can reduce costs, automate workflows, and gain insights faster than competitors. A remote AI developer brings global talent and specialized domain expertise to your team.
AI developers build, train, and deploy machine learning models. They work with data scientists, backend teams, and product managers to integrate intelligent features into products. Their responsibilities range from data preparation to model optimization and API deployment.
Junior AI Developer
Clean and preprocess datasets
Train basic models using scikit-learn or TensorFlow
Support experimentation and evaluation
Mid-Level AI Developer
Design and tune ML/DL models
Implement end-to-end pipelines
Integrate models into production using APIs or microservices
Senior AI Developer
Architect large-scale ML infrastructure
Select and customize model architectures (e.g., CNNs, Transformers)
Guide team on ethical AI, security, and explainability
Junior developers need guidance on architecture and deployment. Mid-levels work independently on models and integration. Seniors lead research-to-production workflows and infrastructure design.
AI developers must be fluent in Python, the dominant language in machine learning. Knowledge of libraries like NumPy, pandas, and scikit-learn is essential.
TensorFlow / PyTorch – Deep learning frameworks for model building
Hugging Face Transformers – Pretrained models for NLP tasks
OpenCV – Popular for image and video processing
Keras – High-level API for rapid prototyping
Jupyter Notebooks – For prototyping and data visualization
Weights & Biases / MLflow – Track experiments, metrics, and model versions
Docker / Kubernetes – Package and deploy models at scale
FastAPI / Flask – Serve models via APIs
ONNX / TensorRT – Model conversion and optimization
AWS SageMaker / Google Vertex AI – Managed platforms for training and inference
Git for versioning and collaborative workflows
Unit and integration testing for reproducibility
Data validation and pipeline tests
MLOps principles and CI/CD for models
Vector databases and embeddings for retrieval-augmented generation (RAG)
Handling drift, monitoring, and model retraining
Critical thinking – Model evaluation and bias analysis
Collaboration – Communicating results with cross-functional teams
Adaptability – Staying current with fast-changing AI trends
More: Universal Behavioral Competencies | Teamwork Template
Choosing the right model depends on your use case maturity and internal expertise.
Ideal for one-off projects, PoCs, or model tuning tasks.
Pros: Fast onboarding, flexible contracts
Cons: Limited post-deployment support
Best for data-first companies or core AI products.
Pros: Long-term ownership and knowledge transfer
Cons: High cost and longer hiring cycles
Perfect for companies that need reliable, production-ready AI talent fast.
Pros: Pre-vetted AI developers, full team or individual, IP and contracts handled
Cons: Slightly higher hourly cost than freelancers
FatCat Remote – Remote AI developers with MLops and LLM experience
Kaggle, Papers with Code, Hugging Face Hub
GitHub repositories with ML projects
Ask peers in tech, research, or academia for candidates with real-world deployment experience.
Assign an NLP or computer vision task with clear goals
Request model evaluation metrics and explainability analysis
How would you prevent model overfitting?
What’s the difference between classification and regression?
Explain how a CNN works.
How would you deploy a model to scale?
What is the role of embeddings in retrieval?
Compare PyTorch and TensorFlow.
What is prompt tuning vs fine-tuning in LLMs?
How do you debug a poorly performing model?
What is your approach to explainability?
What steps do you take to ensure data and model quality?
Describe a time your model didn’t work—what did you do?
How do you communicate model trade-offs to stakeholders?
Learn more:
Junior: $100,000–$130,000
Mid-Level: $130,000–$160,000
Senior: $160,000–$200,000+
Junior: $40–$60/hour
Mid-Level: $60–$85/hour
Senior: $85–$120/hour
$45–$90/hour for strong MLOps, LLM, or deep learning profiles
Compare: Developer Rates Comparison
Automate repetitive tasks and improve efficiency
Personalize user experience at scale
Drive decision-making with predictive insights
Launch new products powered by generative AI and recommendation models
Healthcare – Diagnostics, image analysis, medical record NLP
Finance – Fraud detection, portfolio optimization, risk modeling
E-commerce – Recommendations, dynamic pricing, demand forecasting
Speed: Match with an AI developer in 48 hours
Trial: 30-day no-risk period
Quality: Only top-tier engineers with proven model deployment experience
Flexibility: Hire part-time, full-time, or by project
Compliance: We handle all legal, IP, and payroll overhead
Our work-proven AI Developers are ready to join your remote team today. Choose the one that fits your needs and start a 30-day trial.
Read answers to the most common questions about hiring AI Developers.
AI is revolutionizing businesses by automating tasks, enhancing decision-making, and improving user experiences. Whether you're developing a customer-facing chatbot, predictive analytics tool, or process automation system, AI helps boost efficiency, reduce costs, and uncover valuable insights. Companies across industries—from healthcare and finance to e-commerce and manufacturing—are leveraging AI to stay competitive and drive innovation.
One of AI’s biggest advantages is its ability to process massive amounts of data in real-time, uncovering patterns that humans might miss. Machine learning models improve over time, leading to smarter automation and better accuracy. AI-powered predictive analytics can help businesses forecast trends, while natural language processing (NLP) enables more human-like interactions in chatbots and virtual assistants. With frameworks like TensorFlow, PyTorch, and OpenAI’s GPT models, AI development is now more accessible than ever.
We prioritize fast and efficient hiring. Depending on your requirements, FatCat Remote can connect you with skilled professionals within 48 hours, ensuring your project gets started without delay. After sharing your project requirements, the team quickly matches you with a developer who fits your needs. The streamlined hiring process eliminates delays often found in traditional recruitment, allowing you to integrate a new team member almost immediately.
Yes, FatCat Remote offers flexible hiring options, including hourly, part-time, and full-time engagements, as well as project-based contracts to meet your business needs.
The 30-day trial period for hiring a AI developer on FatCat Remote allows clients to test our service with minimal risk. During this trial, which lasts up to 160 working hours or 30 calendar days (whichever comes first), you have the flexibility to cancel the engagement or request a developer replacement without significant financial consequences. To cancel, you must provide a detailed written notice (via email) at least five working days before the cancellation date, outlining reasons for dissatisfaction such as unmet goals, technical issues, or poor communication.
FatCat Remote follows a multi-step vetting process to ensure only highly skilled developers join their network. The process includes:
Technical Screening: Developers undergo in-depth coding assessments and problem-solving tasks to test their expertise in relevant technologies.
Live Coding Interviews: Candidates participate in real-time coding sessions to evaluate their thought process, coding efficiency, and problem-solving skills.
Soft Skills & Communication Evaluation: Since developers work directly with clients, they are assessed for teamwork, communication clarity, and professionalism.
Experience & Background Check: FatCat Remote verifies work history, past projects, and client feedback to ensure credibility and reliability.
Only candidates who excel in all these areas are accepted into the network, ensuring that businesses get access to top-tier talent.
The cost to hire an AI developer can vary widely depending on a number of factors, including their level of experience, the complexity of the project, the location of the developer, and whether they are freelance or part of a company. Here are some general guidelines:
Experience Level:
Entry-Level: An AI developer with less experience might charge or earn between $60,000 to $90,000 per year in the U.S.
Mid-Level: Developers with a few years of experience might charge or earn between $90,000 to $130,000 per year.
Senior-Level: Highly experienced AI developers can command salaries upwards of $130,000 and can also go as high as $200,000 or more annually.
Freelance Rates: Freelancers usually charge hourly rates, which can vary:
Junior Freelancers: $20 - $50 per hour.
Mid-Level Freelancers: $50 - $100 per hour.
Senior Freelancers: $100 or more per hour.
Location:
Developers in countries with a higher cost of living (like the U.S., Canada, UK, etc.) typically charge more than those in countries with a lower cost of living (like India, Eastern Europe, etc.).
Project Scope and Complexity: Larger and more complex projects will require more experienced developers and potentially a team, which increases costs.
Specific Skills: Specialized skills, such as expertise in machine learning, natural language processing, or computer vision, can also affect costs.
Check out the FatCat Remote pricing model for more information.
An AI developer is responsible for designing, developing, and implementing artificial intelligence solutions. Their work typically involves the following tasks:
Understanding Requirements: Collaborate with stakeholders to determine the specific needs and objectives for AI applications.
Research and Development: Stay current with advancements in AI technologies, algorithms, and methodologies to create innovative solutions.
Design and Architecture: Design the system architecture and plan the implementation of AI models and algorithms.
Data Collection and Preparation: Gather, clean, and organize data needed to train and test AI models.
Model Training: Develop and train machine learning and deep learning models using appropriate frameworks and tools.
Testing and Validation: Test models to ensure they perform as expected and refine them to improve accuracy and efficiency.
Integration and Deployment: Deploy AI solutions into existing systems or create new applications, ensuring seamless integration.
Performance Monitoring: Continuously monitor the performance of AI systems to improve accuracy and efficiency over time.
Documentation: Maintain comprehensive documentation of AI models, processes, and methodologies for future reference and compliance.
Collaboration: Work with data scientists, software engineers, and other professionals to deliver AI solutions.
Ethical Considerations: Ensure AI applications are developed and implemented ethically, focusing on fairness, transparency, and accountability.
An AI developer often requires strong skills in programming languages like Python, knowledge of machine learning frameworks, and a solid understanding of mathematics and statistics.