Deep learning is a subset of machine learning focusing on artificial neural networks with many layers. These networks are designed to process and analyze large volumes of data, enabling machines to perform image and speech recognition and natural language processing. Deep learning has gained significant traction in recent years due to its ability to solve complex problems and drive innovation across various industries.
As this field continues to penetrate industry verticals, the demand for skilled developers with expertise in this field is rapidly growing. Deep learning developers are crucial in designing, implementing, and optimizing artificial neural network models that power advanced AI applications.
Let’s answer pertinent questions about deep learning specialists, their job responsibilities and skill set, and how businesses can acquire top talent in the competitive environment.
Who are deep learning developers?
Deep learning developers are specialized Artificial Intelligence (AI) professionals who focus on creating, implementing, and refining deep learning models. They leverage their machine learning, computer science, and data analysis expertise to develop cutting-edge AI applications to solve complex problems and drive business innovation. They often have a strong foundation in mathematics, statistics, and programming. Many deep-learning developers also gain additional knowledge and skills through specialized courses, certifications, and practical experience in the field.
Deep learning developers are in high demand across various industries, including:
- Healthcare, to help professionals deal with medical image analysis, drug discovery, and personalized medicine
- Finance. Fraud detection, algorithmic trading, and risk assessment
- Automotive. Self-driving vehicles and advanced driver assistance systems
- Retail. Customer behavior analysis, demand forecasting, and inventory management
- Manufacturing. Quality control, predictive maintenance, and supply chain optimization
As AI and ML technologies advance, organizations look for new applications and opportunities, organically increasing the demand for a skilled deep learning developer.
Read more: Deep learning in manufacturing: Unlocking new opportunities for efficiency and quality
Job responsibilities of deep learning developers
Deep learning means that a computer or a machine learns similarly to humans in this technological domain. Thus, engineers have developed a detailed set of responsibilities that help shape this process. Below, you’ll find the typical industry responsibilities of deep learning specialists.
1. Design and implementation
A deep learning developer creates and implements architectures tailored to specific tasks or problems. It involves selecting appropriate neural network types, determining the number of layers and nodes, and configuring hyperparameters to achieve optimal performance. A strong understanding of frameworks like TensorFlow and PyTorch is essential for creating effective models.
2. Data preparation and preprocessing
Developers must preprocess and clean data for training and testing AI/ML models. This may involve data augmentation, normalization, or transformation to ensure the data is structured correctly and error-free. A solid background in data manipulation and analysis and proficiency in programming languages like Python or R is necessary for handling large datasets and preparing them for model training.
3. Model evaluation and optimization
After implementing a deep learning model, developers must evaluate its performance using appropriate metrics and benchmarks. Based on these evaluations, they may need to fine-tune the model's parameters, alter its architecture, or adjust the training process to improve its accuracy and efficiency. Familiarity with evaluation techniques, such as cross-validation and confusion matrices, is crucial for optimizing model performance.
4. Collaboration with cross-functional teams
A deep learning developer often works closely with data scientists, software engineers, product managers, and other stakeholders to integrate their models into broader systems or applications. These specialists communicate their findings effectively, address challenges, and adapt their models to meet the requirements of different projects. Strong collaboration and communication skills are essential for working in cross-functional teams.
5. Staying updated with the latest research and advancements
The field of deep learning is in a constant state of evolution, with new techniques, algorithms, and tools being developed regularly. As a result, staying up-to-date with the latest research and advancements is crucial for engineers to ensure their work is effective. Your tech vendor’s team has to commit to staying engaged with the AI research community. It ensures that their expertise is up-to-date with recent industry advancements.
Read more: The what, why, and how of machine learning and deep learning
The essential skill set of deep learning developers
The position of a deep learning developer requires specific skills to build robust models that meet clients' needs. Below, you’ll find the list of the most-wanted skills you should look for when searching for experts.
Programming languages
Many specialists consider Python the primary language for AI development due to its extensive libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn) that simplify AI development. Its readability and flexibility make it a popular choice among deep learning experts. While not as common as Python, C++ is valued for its performance advantages, particularly in resource-intensive applications. Familiarity with C++ can be beneficial when optimizing deep learning models or working with low-level AI libraries.
Deep learning frameworks
Several powerful open-source libraries are available for machine learning and deep learning tasks. TensorFlow is one such library that offers flexibility in deployment across platforms and simplifies complex neural network creation with its computational graph abstraction.
PyTorch is another popular option, offering a dynamic computation graph and user-friendly interface that supports research-oriented tasks and robust debugging capabilities. Keras is a high-level neural network API that provides a simple and intuitive interface for developing AI/ML models and can be used on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano for added versatility.
Data manipulation and visualization tools
Regarding visualization tools, Pandas is a widely-used Python library for data manipulation and analysis, providing data structures like DataFrame and Series for handling large datasets and performing operations like filtering, aggregation, and transformation. Furthermore, as a fundamental library for scientific computing in Python, NumPy supports multi-dimensional arrays, matrices, and various mathematical functions to perform operations such as linear algebra, statistical analysis, and numerical optimization.
Matplotlib is also a popular Python library for creating static, animated, or interactive visualizations, helpful in exploring data, presenting findings, and visualizing model performance during development. Built on top of matplotlib, Seaborn is a statistical data visualization library that simplifies the creation of aesthetically pleasing plots, providing built-in themes, color palettes, and functions for everyday statistical tasks.
Cloud computing platforms
Any skilled deep learning developer should also have sufficient knowledge of Cloud environments. For instance, Amazon Web Services (AWS) is a leading Cloud platform that offers a suite of machine learning and AI services like Amazon SageMaker, which simplifies the process of training, tuning, and deploying models at scale.
Google Cloud Platform (GCP)'s AI Platform provides an end-to-end solution for building, training, and deploying machine learning models. It also offers specialized tools like Cloud AutoML for generating custom deep-learning models without requiring extensive expertise.
Microsoft Azure's Machine Learning is a comprehensive Cloud-based service that supports the entire model lifecycle, from data preparation and model training to deployment and management. It also integrates with popular frameworks like TensorFlow and PyTorch.
Read more: Comparing the Big 3: AWS vs Azure vs GCP
How to hire a deep learning developer?
Partnering with a team of deep learning experts can be challenging since the number of companies offering this expertise can be overwhelming. The following recommendations are meant to help you in your search process and make it more seamless and straightforward:
1. Define specific needs. Begin by identifying your organization's specific deep-learning requirements and goals. Assess the scope of the project, the desired outcomes, and any unique challenges that may arise during the development process. Determine the essential technical skills needed for your project, such as proficiency in programming languages, deep learning frameworks, and data manipulation tools. Specialists with a background in your domain can better understand your needs and contribute more effectively to the project's success.
2. Look for specific deep learning technologies. Identify the applications and technologies most relevant to your project, such as natural language processing, computer vision, or reinforcement learning. This will enable you to target developers who specialize in these areas. Research and familiarize yourself with the latest developments in these applications and technologies to understand their capabilities and limitations.
3. Evaluate technical competence. Review the vendor’s expertise, the range of projects delivered, and their approaches. Doing so, you’ll get an idea of their technical capacities and their work approaches. You can also request problem-solving deep-learning scenarios to assess your tech partner’s expertise during negotiation.
4. Check expertise and review references. Verify tech partners’ expertise in the field by reviewing their specialists’ educational backgrounds, certifications, and professional experience. Look for companies that have demonstrated a commitment to staying up-to-date with the latest industry advancements. Before you hire a deep learning developer, request references from past clients or employers to gain insights into the performance, work ethic, and ability to deliver high-quality results. This information can help you make a more informed decision when selecting developers for your project.
5. Negotiate on the contract type. Review the proposed contract thoroughly to ensure that it covers all aspects of the project, including timelines, deliverables, payment terms, and intellectual property rights. Make sure that both parties have a clear understanding of their responsibilities and expectations. Negotiate the contract type that best suits your project and organization, which can be either Fixed Price or Time & Material.
Read more: Software development outsourcing: how to avoid contract loopholes
The success story of teaming up with deep learning developers
Redflex is an Australian-based company that develops intelligent transport solutions (ITS) for government, police, and traffic departments worldwide. Redflex wanted to increase its market presence by creating a new intelligent transport solution for traffic management. The client aimed to validate its product idea and develop a solution to detect traffic anomalies accurately while adhering to various countries' traffic rules and related policies.
N-iX built a team of computer vision specialists and began the project with a proof of concept (PoC). They collaborated with the client and its team in Australia to work on seatbelt fastening detection and capturing distracted driving behaviors. The N-iX team employed computer vision and deep learning to develop the PoC. We offered several options for neural networks with different approaches to network training, which allowed the client to identify a person behind the wheel. The team also worked on another PoC that allowed capturing specific behaviors of distracted driving, such as talking or texting on the phone, eating and drinking, etc. For added security, N-iX engineers used AWS virtual machines as workstations.
Value delivered:
- Validated Redflex's product idea to expand into new markets;
- Built a PoC of a system that automatically detects seat belt fastening violations with an accuracy of approximately 88%;
- Built a PoC of a system that captures distracted driving behaviors with a person detection accuracy of 91% both at night and during the day;
- Developed and trained a model that supports real-time streams, helping create fines in real-time based on captured data and specific violations;
- Improved road safety by developing a solution that accurately detects traffic anomalies, preventing accidents on the road;
- Improved transport system efficiency by developing a real-time stream solution, helping generate fines in real-time based on captured data.
Keep reading: Increasing market reach with traffic management and computer vision
Wrapping up
Deep learning developers play a critical role in unlocking the potential of artificial intelligence and machine learning. Hiring the right talent for your organization's projects is essential to achieving success and staying competitive in your industry. You can increase your chances of finding the perfect match for your project by:
- Defining your specific needs
- Targeting relevant applications and technologies
- Evaluating technical competency
- Checking expertise and references
- Negotiating the right contract type
Partnering with a reliable software services provider like N-iX can also help you tap into a pool of experienced deep learning engineers with a proven track record in delivering high-quality solutions. N-iX is a global software solutions and engineering services company that helps leading organizations turn their business challenges into long-term value, operational efficiency, and revenue growth by leveraging modern technology.
Reasons to team up with deep learning developers at N-iX
- N-iX is a leading Big Data vendor with over 10 years of proven expertise in data-related services.
- The company boasts a team of more than 200 data specialists skilled in Big Data, AI/ML, legacy migration, Data Science, and Data Analytics, among others.
- N-iX provides technology consulting and implementation of intelligent solutions powered by AI and ML, including deep learning, predictive analytics, customer behavior analysis, risk allocation and mitigation, and predictive maintenance.
- N-iX's Big Data services clients include Orbus Software, Gogo, Lebara, AVL, cleverbridge, and Fortune 500 companies.
- N-iX has been consistently recognized by CRN as one of the leading solutions providers in North America, earning spots on the Solution Provider 500 and CRN Fast Growth 150 lists.
- The company adheres to industry regulations and standards such as PCI DSS, ISO 9001, ISO 27001, and GDPR.
