Hire AI Engineers

Improve your operations and business performance thanks to AI engineering

Unleash the potential of Artificial Intelligence (AI) to transform your business. Our state-of-the-art solutions are crafted to tackle challenges, streamline operations, and elevate customer journeys. Through AI-powered systems, we excel in providing personalized product suggestions, accurately recognizing objects in images, and optimizing routine tasks to maximize efficiency.

Increase sales. 49% of customers are willing to purchase more frequently when AI is present.
Improve productivity. Artificial Intelligence technologies are projected to increase labor productivity by up to 40% by 2035.
Analyze large volumes of data. AI gives apps the ability to learn and improve over time. It is extremely adept at quickly identifying patterns and trends.
Improve customer satisfaction. 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.

Airbnb enjoys nearly a 4% lift in booking conversions thanks to AI solutions

Products and marketing improved with AI algorithms.

Airbnb has successfully used AI to create new products, improve its service, and take advantage of new marketing strategies. In addition, it has leveraged machine learning to detect host preferences.

For each search query that a guest enters, Airbnb’s model computes the likelihood that relevant hosts will want to accommodate the guest’s request. In A/B testing, the model showed about a 3.75% increase in booking conversions, resulting in many more matches on Airbnb.

Man holding a mobile phone with airbnb app

What started as a small research project resulted in the development of a machine learning model that learns our hosts’ preferences for accommodation requests based on their past behavior. The model showed about a 3.75% increase in booking conversions, resulting in many more matches on Airbnb.

$Bar Ifrach photo$
Bar Ifrach
Former Director of Data Science at Airbnb

Data Engineering. Prepare your data to make the most of AI algorithms
Data Science. Uncover meaningful insights to improve your products or services
Recommender Systems. Create a personalized experience for every user, thanks to an accurate recommendation system
Natural Language Processing. Build natural interactions with your users and identify patterns in unstructured data
Computer Vision. Automate difficult decision-making processes based on images
Audio Recognition. Identify patterns in audio data, enabling voice communication using a range of devices

The right process is a key advantage

Over the years, our team has developed and implemented a robust and efficient workflow for AI projects. Our processes ensure our customers receive more reproducible results faster and in a more flexible way.

Our workflow focuses on three stages:

The Role of AI Engineers

Contents

An AI Engineer is a tech enthusiast who is deeply entrenched in the world of Artificial Intelligence (AI) and Machine Learning (ML). They are driven by a relentless passion to leverage AI to create valuable products that revolutionize business operations.

Their role sits at the intersection of data science, software engineering, and machine learning, carrying immense responsibility and ownership. They must stay agile, just like the technology they work with, which is why we at Netguru endorse the Agile methodology.

Agile methodology is a streamlined process that turns a vision for an AI-driven solution into a working product. It involves continuous planning, learning, improvement, and collaboration within a self-organizing team, paving the way for iterative development and delivery.

AI Engineering serves as a bridge between business objectives, user experience, and technical development, driven by innovation. An AI Engineer helps to:

Achieve business objectives
Bridge communication gaps
Align a common vision with the tech team
Focus on user needs
Represent the user inside a company

Why Research is Vital for AI Implementation

In-depth research lies at the core of an AI Engineer's responsibilities. Being the voice of technological innovation requires extensive and up-to-date knowledge about AI and ML trends, coupled with an ability to identify significant technological shifts.

Research into AI implementation offers profound insights that facilitate the decision-making process. Vital information can also be gleaned from a thorough analysis of the AI landscape. Data-driven qualitative and quantitative research helps you make the right AI investment decisions.

Why Validation is Critical for AI Solutions

An idea that is not validated is merely a hypothesis. A key responsibility of an AI Engineer is to probe, question, and find answers. Before implementing an AI solution or a new feature, it's crucial to validate the existing problem and the proposed solution for specific target groups. User feedback helps in steering the AI solution in the right direction or pivoting when necessary.

Various experiments and methods can validate your assumptions. However, poorly designed experiments can lead to misleading and damaging results. An AI Engineer is skilled in conducting both low-fidelity experiments that require minimal investment and complex, high-fidelity experiments that suit the product's needs. Credible validation minimizes the risk of AI solution failure and enhances the likelihood of success.

The Key Objectives of AI Engineering

AI Engineering is a function within a business that deals with AI-driven product development and its justification. It focuses on implementing a new and effective AI solution or enhancing an existing one. The primary objectives of AI Engineering include:

Building AI Solutions
Measuring AI Effectiveness
Improving AI Functionality

The primary aim of AI Engineering is to find answers to critical AI questions: What problem does our AI solution solve? How does it achieve this more effectively than other methods?

The AI Engineering process is about finding the right audience for your AI solution and identifying key features that resonate with the audience. It's a crucial step in validating your business idea to increase your chances of success.

The Importance of Communication in AI Engineering

Dealing with numerous internal and external stakeholders requires excellent communication skills. Good storytelling, public speaking skills, and emotional intelligence play a significant role in the everyday life of an AI Engineer.

A universal tool to maintain transparency and clarity within an organization is an AI roadmap, which represents a high-level concept of the AI solution. An AI Engineer ensures every person in the organization is aligned with a common goal.

Application of AI Engineering in Different Industries

Businesses in all industries can benefit from AI engineering services. Every business sells some sort of product or service that requires a unique approach and specific knowledge. Finance is one example.

In a fintech product, for example, it is crucial to cope with legal affairs and security, while in healthcare, AI-driven solutions need to comply with regulatory requirements and patient privacy laws. Retail businesses can leverage AI to enhance user experience and streamline internal processes.

Netguru specializes in AI engineering for a variety of industries, from the finance sector to healthcare, retail, and beyond.

AI Algorithms and Learning Methods

Clustering techniques like density-based, hierarchical, and partitioning methods are used to group datasets into clusters based on their similarity. Metric learning, on the other hand, focuses on distance metrics to identify similarities or differences between data points.

Furthermore, cutting-edge techniques like few-shot learning have emerged, allowing AI models to understand and make accurate predictions from very few examples. This ability significantly reduces the need for large amounts of training data and accelerates the learning process.

Scikit-learn and Keras are examples of libraries offering a host of algorithms for various machine learning tasks. Scikit-learn provides simple and efficient tools for predictive data analysis, including clustering, regression, and classification algorithms. Keras, a user-friendly neural network library, is built over TensorFlow and Theano, specifically designed to enable fast experimentation with deep neural networks.

By integrating these cutting-edge tools and resources, Netguru's team of seasoned AI Engineers ensures the development of powerful AI solutions that are tailored to your business needs, heralding a smarter, AI-driven future.

In conclusion, AI Engineering is a dynamic and continually evolving field, powered by a range of tools, frameworks, and algorithms. Netguru's team of experienced AI Engineers harness these resources to deliver AI solutions tailored to your business needs, paving the way for a smarter, AI-driven future.

Why Hire Netguru for AI Engineering Services?

When you partner with Netguru throughout the entire AI lifecycle, you have a reliable ally dedicated to helping you create well-researched and effective AI solutions. We offer end-to-end AI development services, meaning our skilled team is here throughout the process, from validating your ideas and devising an AI strategy to ensuring your AI implementation is successful.

Our AI engineers and teams are equipped to identify market gaps, research user needs, and make sure you achieve an AI-market fit. They're also responsible for building AI roadmaps and ensuring the process runs smoothly, whether you're implementing an AI solution from scratch or improving an existing one.

Reach out to Netguru today and leverage our expertise to create powerful AI-driven solutions that revolutionize your business operations.

Practical AI focus. We specialize in translating tech trends into practical and commercially viable AI applications.
Diverse AI team. Diverse group of experts, including researchers, product managers, and engineers, ensuring comprehensive solutions.
Industry Experience. Hands-on exprience with unique challenges and opportunities in Finance, Retail, Proptech, and Healthtech.
One-stop Partner. Comprehensive range of services, including AI, consulting, product design, mobile and web development and more.

World-class specialists at your disposal

Our developers are open source contributors with more than 220 repositories on GitHub.

We constantly invest in developing new technologies and testing solutions in our R&D department, sharing our experience both on expert blogs and at various tech industry conferences such as IGARSS, AAIA, and MICCAI.

Our team has worked on a variety of engagements, including many end-to-end projects.

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My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.

Gerardo Bonilla
Product Manager at Moonfare
Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.

Adi Pavlovic
Director of Innovation at KW
Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.

Manon Roux
Founder at Countr

15+

Years on the Market
400+

People on Board
2500+

Projects Delivered
73

Our Current NPS Score

$5M

Granted in funding for Shine. Self-care mobile app that lets users practice gratitude
$28M

Granted in funding for Moonfare. Investment platform that enables investment in private equity funds
$20M

Granted in funding for Finiata. Data-driven SME lending platform provider
$47M

Granted in funding for Tourlane. Lead generation tool that helps travelers to make bookings

What are AI Primer Workshops?

Embark on a transformative 6-hour workshop that delves into the vast possibilities of AI adoption. Experience a profound exploration of AI's potential, culminating in a personalized report brimming with ingenious AI implementation ideas tailored to your business's unique impact and effort.

What is AI Solution Design Sprint?

Experience a transformative service that takes your business challenges and turns them into actionable AI solutions within just two days. Our comprehensive workshop includes seamless integration into your existing ecosystem, solution comparison, data governance strategy, risk identification, and implementation of a robust monitoring strategy.

What is Data Engineering?

We provide a customized solution to build a strong data foundation for your business, offering intricate data transfers, consolidation and integration of data sources, construction of data lakes and warehouses, and optimization of data pipeline performance.

What is AI Proof-of-Concept?

A time-constrained validation procedure that evaluates the viability of incorporating an AI solution with your data, mitigating potential risks, and accelerating outcomes for your comprehensive machine learning project.

What is AI MVP Implementation?

A cutting-edge solution that seamlessly connects the prototype and production stages for ML models, enhancing their speed, accuracy, cost-efficiency, and various other deployment aspects.

Why AI Engineers are Emerging Now?

AI Engineering is rapidly becoming a crucial field due to a few key reasons: Foundation Models: These models are "few shot learners," showing in-context learning and zero-shot transfer capabilities. The creators of these models can't fully predict their capabilities. Non-researchers can find and exploit these models' capabilities simply by spending more time with them and applying them to under-explored domains. Access to AI Research as a Service: Tech giants like Microsoft, Google, and Meta have cornered the scarce AI research talent. This has led to the emergence of "AI Research as a Service" APIs. As there are far more software engineers than AI researchers globally, supply constraints dictate that an "in-between" class of AI Engineers will rise to meet demand. GPU Hoarding: Due to the growing demand for processing power, companies are increasingly hoarding GPUs. The global chip shortage is inadvertently creating even more scarcity, setting the stage for a rise in AI Engineers who can efficiently use these resources. Fire, Ready, Aim Approach: Instead of requiring laborious data collection before training a specific model, AI Engineers can use Large Language Models (LLMs) to prototype and validate product ideas quickly. This approach allows AI Engineers to validate AI products cheaper and faster, promoting an Agile approach to AI. Language Shift: The AI community is traditionally Python-centric, but with the rise of tools catering to JavaScript developers, such as LangChain.js and Transformers.js, the Total Addressable Market (TAM) has dramatically expanded. Generative AI vs Classifier ML: AI Engineers are shifting from traditional machine learning tasks to more generative tasks, using AI models to build innovative applications such as writing apps, personalized learning tools, natural language spreadsheets, and visual programming languages. The rise of AI Engineers highlights the growing demand for experts who can effectivelyleverage these advanced tools and models. As we continue to push the boundaries of what AI can achieve, the role of the AI Engineer will undoubtedly become even more critical.

What are AI Development Tools and Frameworks?

AI algorithms and learning methods are the driving forces behind any AI solution. For instance, supervised learning involves training an AI model with labeled data, while unsupervised learning works with unlabeled data to find hidden patterns and structures. Clustering techniques like density-based, hierarchical, and partitioning methods are used to group datasets into clusters based on their similarity. Metric learning, on the other hand, focuses on distance metrics to identify similarities or differences between data points. Furthermore, cutting-edge techniques like few-shot learning have emerged, allowing AI models to understand and make accurate predictions from very few examples. This ability significantly reduces the need for large amounts of training data and accelerates the learning process. Scikit-learn and Keras are examples of libraries offering a host of algorithms for various machine learning tasks. Scikit-learn provides simple and efficient tools for predictive data analysis, including clustering, regression, and classification algorithms. Keras, a user-friendly neural network library, is built over TensorFlow and Theano, specifically designed to enable fast experimentation with deep neural networks. By integrating these cutting-edge tools and resources, Netguru's team of seasoned AI Engineers ensures the development of powerful AI solutions that are tailored to your business needs, heralding a smarter, AI-driven future. In conclusion, AI Engineering is a dynamic and continually evolving field, powered by a range of tools, frameworks, and algorithms. Netguru's team of experienced AI Engineers harness these resources to deliver AI solutions tailored to your business needs, paving the way for a smarter, AI-driven future.

What are Advanced AI Models?

In the realm of advanced AI models, AI Engineers have a diverse array of resources at their disposal. They are capable of evaluating anything from the largest AI models such as OpenAI's GPT-4 and Google's Claude, down to open-source alternatives like Huggingface, LLaMA, and more. GPT-4, the fourth iteration of the Generative Pretrained Transformer models by OpenAI, is a cutting-edge language model that utilizes machine learning to produce human-like text. Claude, developed by Google, showcases another leap in language understanding capabilities. Open-source models such as Huggingface and LLaMA provide accessible and efficient solutions for AI development. Huggingface, a transformative NLP library, enables developers to build, train and deploy state-of-the-art models. LLaMA, or the Language Model for Many Applications, is another impressive tool that has made significant strides in Natural Language Processing (NLP).

What is Data Warehousing and Management?

The backbone of any successful AI solution lies in effective data warehousing and management. By securely storing and managing massive volumes of structured and unstructured data, businesses can derive actionable insights and predictions through AI. Data warehousing solutions allow for the secure storage, retrieval, and management of data, empowering AI Engineers to work with reliable datasets for accurate AI modeling and predictions. When it comes to data warehousing and management, solutions like Google BigQuery and Amazon Redshift come to the forefront. BigQuery is a fully-managed, serverless data warehouse that enables super-fast SQL queries and interactive analysis of massive datasets. Amazon Redshift is a cloud-based data warehousing solution from AWS, designed for high-performance analysis of large datasets, making it an ideal choice for AI applications.

What are Cloud Computing Platforms?

Cloud computing platforms play a vital role in AI development, offering scalable, secure, and cost-effective computing resources. These platforms provide the necessary computational power and storage capacity required to train and deploy complex AI models. They offer services like machine learning platforms, data warehousing, data analytics, and many more, making them indispensable for AI development. In the realm of cloud computing, AWS, Google Cloud, and Microsoft Azure lead the way. These platforms offer an extensive range of AI and machine learning services. For instance, Azure Machine Learning service provides a comprehensive workspace for building, training, and deploying machine learning models, while Google Cloud's AI Platform unifies the process from ideation to deployment.

What are Data Visualisation and Reporting tools?

Visual data representations make it easier to understand complex datasets and AI model outcomes. Data visualization and reporting tools convert raw data into interactive charts, graphs, and reports, making it easier for decision-makers to comprehend the results and make informed decisions. Data visualization tools like Tableau and Power BI provide comprehensive reporting solutions. Tableau is known for its intuitive, drag-and-drop interface and wide range of beautiful visualization options. Power BI, a business analytics tool from Microsoft, offers interactive visualizations with self-service business intelligence capabilities.

What are Machine Learning Platforms?

Machine learning platforms are comprehensive toolkits that allow AI Engineers to design, train, validate, and deploy machine learning models. They offer a host of features such as pre-built algorithms, drag-and-drop model builders, and automation capabilities, streamlining the entire machine learning process. Regarding machine learning platforms, RapidMiner and KNIME are prominent choices. RapidMiner offers a platform that covers the entire life-cycle of prediction modeling, including data loading, transformation, model building, output, and visualization. KNIME is a free and open-source data analytics tool that allows users to visually create data workflows, making it a go-to choice for many AI engineers.

What are the most impactful skills in AI Engineering?

AI Engineering combines the domain of software development with an intricate understanding of artificial intelligence technologies. The most impactful skills in this realm include proficiency in AI development, in-depth knowledge of AI tooling, fluency in Cloud AI services, and an understanding of Diffusion Models and Large Language Models (LLMs). An AI engineer should be able to design, develop, and maintain AI models, incorporate them into software applications, and work with cloud AI services to enable scalability and efficiency. It's also essential to have a solid grasp of Diffusion Models and LLMs as they form the backbone of many modern AI solutions.

What is AI Development?

AI Development is the process of designing, building, and training artificial intelligence models to solve complex problems. It involves several key tasks: Feature Engineering: This is a vital process in machine learning where raw data is transformed into features that better represent the underlying problem to the machine learning algorithms, thus improving their performance. Prompt Engineering: In the context of natural language processing, prompt engineering involves fine-tuning the prompts given to a language model to enhance its performance and accuracy in generating desired outputs. Rapid Prototyping: A necessary skill in AI development, it pertains to the ability to quickly build a minimum viable product (MVP) using various tools and techniques, thereby saving time and resources. Validating Correctness: This is the application of techniques to verify the correctness of an AI solution. For example, preparing a validation dataset to assess the performance and accuracy of a trained model.

What is AI Tooling?

AI tooling refers to the suite of software tools and libraries that aid in developing, deploying, and maintaining AI models. Some noteworthy examples include: HuggingFace: An open-source provider of natural language processing (NLP) models and datasets, HuggingFace is widely known for its Transformers library, which offers a broad range of pre-trained models for tasks like text classification, question-answering, and more. LangChain: This intuitive open-source framework was created to simplify the development of applications using large language models (LLMs), such as OpenAI or Hugging Face. Langchain.js: A version of LangChain designed for JavaScript developers, it simplifies the development of applications using LLMs. Microsoft Guidance: An advanced tool that enables you to control modern language models more effectively and efficiently than traditional prompting or chaining methods. tiktoken: A fast Byte Pair Encoding (BPE) tokeniser, tiktoken is designed for use with OpenAI's models, enabling efficient tokenization of input data. Vercel AI SDK: An interoperable, streaming-enabled, edge-ready software development kit for AI apps built with React and Svelte. It helps to streamline and simplify the development of AI applications.

What is Azure OpenAI Service?

Azure OpenAI Service is a large-scale AI model platform designed to power applications with advanced AI capabilities. This Microsoft-owned service leverages vast, generative AI models that have deep understandings of language and code. This empowers developers to build innovative applications that incorporate cutting-edge features like writing assistance, code generation, and reasoning over data. The key advantages of using Azure OpenAI Service include: Large, Pretrained AI Models: Azure OpenAI Service offers access to large and powerful AI models that have been pre-trained on diverse datasets. These models can comprehend language and code at a deep level, unlocking new scenarios for your applications. Custom AI Models: Besides using the pre-trained models, you can fine-tune these models with your own data and hyperparameters to meet your specific needs. This enables you to build AI models that are tailored to your unique use case. Responsible AI Features: The platform comes with built-in features that help ensure the responsible use of AI. It includes capabilities for detecting and mitigating harmful use of AI, thereby fostering trust and safety in your AI-powered applications. Enterprise-Grade Security: Azure OpenAI Service provides robust security measures, including role-based access control (RBAC) and the ability to use private networks. This ensures that your AI models and data are well-protected against security threats. By integrating Azure OpenAI Service into your development pipeline, you gain access to powerful AI capabilities that can significantly enhance your applications' functionality while also benefiting from the robust security and responsible AI measures offered by the platform.