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Leave the hassle of job posting and hire dedicated Ai developers
Hire dedicated AI developers with expertise to drive your projects forward. Get tailored solutions, rapid integration, and exceptional results at competitive rates!
Full Time
Hours Per Day
8 hrs / day
Hours Commitment
160 Hrs
Hourly Basis
Hours Per Day
8 hrs / day
Hours Commitment
50 Hrs Mini
Part Time
Hours Per Day
8 hrs / day
Hours Commitment
60 Hrs
Why choose us as an artificial intelligence development company?
On-time delivery
We ensure your requirements are met on time without compromising innovation or quality.
Scalable
Ensure you have a top-notch, multidisciplinary team that can swiftly adjust to the unique requirements of each AI project.
Easy Migration
It is our integrated AI services from development to deployment that guarantee your project to achieve the best results.
Custom AI Solutions for Your Business
With us, you will be able to work with the most suitable AI strategies and solutions that are in tune with your organization’s needs.
Advanced Technologies
Our AI solutions protect your business from outdated practices while providing cutting-edge artificial intelligence tools and machine learning architectures.
24/7 available
Our team always follows the principles of clear communication and constant support during the specific phases of the project, guaranteeing that your objectives are achieved with outstanding success.
Meet our talent Ai developers for hire
With 8+ years in AI development, Alex specializes in building intelligent systems, deep learning models, and machine learning algorithms for predictive analytics and automation.
Expert In:
Jordan has over 10 years of experience in AI-driven data science, focusing on neural networks, reinforcement learning, and designing robust algorithms for complex problem-solving.
Expert In:
Taylor brings 7+ years of expertise in AI architecture, including designing scalable machine learning systems, robotics automation, and integrating AI technologies across industries.
Expert In:
With 9+ years of experience, Chris excels in natural language processing (NLP), building intelligent chatbots, sentiment analysis systems, and integrating AI into enterprise-level applications.
Expert In:
How We Deliver AI Development Services
Discover
From our team, we will assign a leader who analyses your business concerns, evaluates opportunities and determines the best form of collaboration: managed delivery, consulting, full teams, integrated, or high-end talent.
Define
Whether you require a full end-to-end delivery solution or you are engaging our advisory and consulting services to help work on a new project, you would be partnered with one of our Toptal leaders to frame up your objectives and service requirements.
Develop
Once you have established what your service is, our talented team will structure your project timeline as well as the process and first sketches, be it a model user journey or brand identity that you require for your project.
Deploy
Sourcing talent with you, Toptal will start working, keeping the quality oversight, managing the project, and maintaining the delivery frequency.
Comparative analysis: NGS Solution vs. Other!
| Factor | Value Coders | In-House | Freelancer |
|---|---|---|---|
|
Time to get Right Developers |
1 day – 2 weeks |
1 – 12 weeks |
1 – 12 weeks |
|
Time to Start a Project |
1 day – 2 weeks |
2 – 10 weeks |
1 – 10 weeks |
|
Recurring cost of Training & Benefits |
0 |
$10,000 – $30,000 |
0 |
|
Time to Scale size of the team |
48 hours – week |
4 – 16 weeks |
1 – 12 weeks |
|
Pricing (weekly Average) |
1.5x |
2x |
1x |
Navigate our range of AI solutions
Our Case Study
10X
15X
20X
30X
User Guide To Hire Data Machine Learning Developers
Guides Topics
Our artificial intelligence-related cases
The advantages of generally built Artificial intelligence solutions have been enumerated long ago where they use in different fields to enhance speed of production and quality of the products. Nevertheless, as the process of AI development is not typical for the regular software development operations, it needs to be approached more carefully in order to be effective.
However, what kind of special attention should artificial intelligence projects get and how can companies start on the process of developing the right solutions? This is what we will be exploring , and we will be checking the credibility of the above points some of the research questions are :
Here we present and discuss how development teams worldwide can jump into AI
Introduction to AI Software Development
AI software development is the act of creating a software solution where the central theme of the software is an AI technology. One such solution can be as basic as a chatbot accompanying a company’s customer service all the way up to a classification model applied in health care equipment to identify pathogens. Still, designing a personalized artificial intelligence solution is a challenge that requires a lot of computational power and several weeks of trial when you decide to work with AI development services companies.
It also involves accumulation of a lot of training data and then pre-processing in order to check the feasibility of using it in the model. As with any regular software development project, there may be one or several development methodologies applied to the AI solution. You can get a brief insight of what development methods entails by going through these various different development methodologies.
Popular AI development methodologies
- In the case of development, depending on the size and requirement of deploying the project, different methods are employed. All of these methodologies can be used to construct the same kind of AI solution, but they have different features about how they are used to get to the end result. While some of these methodologies can be clearly distinguished by their termination as in the case of the waterfall methodology other subset of them is iterative that reiterates after every cycle.
- Agile: Born out of one of the most popular development methodologies, Agile Development is one of the fastest and most rapid methodologies suitable of developments. These cycles are client centered, whereby the developers develop features and componets fast and can deploy them to the core applicaiton thus resulting to faster develoment in short cycles.
- DevOps: Not being just a method, but a way people work, DevOps is a methodology that prescribes norms for the team. In DevOps development, the first thing worth emphasizing is automation and quality testing in a development cycle. In larger projects where standardized deployment is necessary, this methodology is ideal with such detailed Standard Operating Procedures as CI/CD.
- MLOps: It is also similar to the DevOps methodology and aims for great quality in the MLOps cycle deployments. It remains in a few extra phases for constructing machine learning models and data modulation. It encourages developing new ML model test cases and is suitable for experimentation.
- Waterfall: Contrary to the other development methodologies the waterfall is strictly a sequential one. It operates through a systematic process of creating the whole application and subjecting it to a series of test and deployment until the instances of maintenance. It does this in order to give it a conclusive end and this is more appropriate in solutions that would require little more than development.
What makes AI software development different?
Apart from the core product, the main difference between the development cycle for an AI project and a normal software solution lies in the level of refinement required. With most normal solutions, there is no concern for discrepancies in the output as there are no external factors that disturb the functions of the application.
AI projects are intrinsically unpredictable as there is constant interaction with human intelligence; this dictates its effectiveness in resolving queries. At times when model drift is observed, the AI developer must interfere to ensure that there is no damage caused. All of this points towards a heightened need for stringent security testing standards and model refinement during the entire AI development process.
A Comprehensive Guide to Machine Learning Development
What is Machine Learning in AI?
Artificial intelligence can be described as the study of methods with which new knowledge can be incorporated to an existing artificially intelligent environment. The AI techniques of machine learning let computer systems imitate the actions of human beings in a way they upgrade the aspects of those systems through discovery of patterns in a particular field.
Since the objective of AI is to replace man and replicate his actions in specific docket and function, machine learning prevents an ML model from understanding matters like human behavior and consequences of certain operations without the accompaniment of a dedicated developer or programmer.ML makes AI increase as a species and become more effective and beneficial the longer it is in existence.ew things without any explicit programming. Machine learning models allow computer systems to mimic humans in a way that they improve their functions through pattern recognition. As the main goal of artificial intelligence is to mimic human beings in certain tasks and actions, machine learning algorithms help AI models understand things such as human nature and the outcomes of their actions without allocating a dedicated developer or programmer. ML allows AI to grow innately and become more powerful and useful with time and experience.
How Does Machine Learning Work?
In order to give the reader an informed insight in how the machine learning development process actually unfolds, the process will here be divided into seven steps to make it easier to grasp.
To better understand how the machine learning development process works, we will break down the whole process into seven steps for easier comprehension.
Step 1: Data collection
Data is the primary ingredient that machine learning models run on. A machine learning model trains on the data we input, and the quality and quantity of the training data determine how effective and accurate the model will be once it’s ready.
Consequently, it is important to choose proper training data for an ML model or create the most suitable one. In this case, good training data will refer to data that is in the right format with appropriate labels and features inclusive of the central aim. This will also depend on the budget that your project will allow, size of the project and perhaps the main aim of the project.
Step 2: Data preparation
Step 3: Choosing the right model
Step 4: Training the model
Step 5: Evaluating the model
When the model is through with the training processes it is always advised to test the performance of the model before issuing it to the field as is done with software through the beta test. This means that it is only safe to take a machine learning system live after having passed through evaluation with some performance.
For instance, Hinton et al. indicate that for a face recognition algorithm, you can use evaluation datasets during this step. An evaluation dataset as a set of images with which you can evaluate an algorithm’s performance. Biasedness, variability, and total aberration are some of the ways by which we can measure ML algorithms.
Step 6: Tuning and optimization
Once you have evaluated the model you can make certain changes to parameters of model if the evaluation performance is not satisfactory. It can also be referred to as ‘hyperparameter tuning’.
The methods of hyperparameter tuning consist in choosing hyperparameters randomly and then testing combinations of them (grid search), or in splitting the collection of data into subsets and training a model with different subsets separately (cross-validation).
Step 7: Predictions and deployment
Case Study: Streamlining Property Appraisals with AI Computer Vision
Problem
Challenge
Scaling operations while upholding the quality of appraisals proved to be a considerable hurdle. The manual validation of images submitted in property appraisals was not only time-consuming but also susceptible to human error. The company required an efficient system to confirm that appraisal photos were accurately categorized by room type and void of prohibited content.
The first iteration of the tool, utilizing a standard object detection model called YOLOv8, was unable to accurately identify certain rooms, especially garages and laundry rooms, while also misclassifying household appliances. This demanded the development of a more customized solution designed for the specific needs of the property appraisal company.
- Outcome
- Increased accuracy in image detection
- Accelerated approval time
- Automated image analysis and validation
- Reduced manual review
- Increased scalability
Solution
We developed a multi-layered object detection system by integrating a custom object detection model, specifically trained to identify and classify objects unique to property appraisals. By utilizing this we preliminary stage of object detection and the custom to capture any remaining nuances, the combination of these two models enhanced the overall performance and effectiveness of the object detection system. This ensemble method provided a sophisticated means to analyze the variety and frequency of objects within a space.
By leveraging Random Forest, a machine learning algorithm designed for classification and regression tasks, the Computer Vision system could determine the room’s function with greater precision and reliability. The solution maximized efficiency, drove scalability, and enhanced the customer experience during property transactions.
FAQS
How to evaluate an ai developers project handling skills
The project handling skill is revealed as the competence of the developer to apply theoretical knowledge into practice. This means that you can check these types of skills by reviewing their portfolios. Thus, having the portfolio, it is possible to learn what kinds of tasks they solved and what kind of solutions the young developers tried to implement.
Moreover, as you will see from the portfolios, you will be armed with a general feel of what a developer is capable of in handling a project.
What are the challenges in AI development?
What skills should an AI developer have for building a website?
What is the Cost of Hiring AI Developers?
When we talk about to hire artificial intelligence developers, the cost depends on the requirement and multiple factors. These factors include the location of the developer, skills and experience, project complexity and the list goes on.
The determinant key feature is the location of the developer. The developer rates fall in the range of $50–$150 per hour. An AI developer in the Asia Pacific region will cost less than the USA, Europe, and Australia.
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