Deep Learning Engineers

Hire Deep Learning Engineers

Deep learning is a technology that leverages machine learning and artificial intelligence (AI) to enable individuals to more effectively acquire knowledge. Data science is a critical aspect of deep learning, integrating both statistical analysis and predictive modelling. For deep learning engineers whose responsibilities include gathering, exploring, and interpreting immense amounts of data, this technology accelerates and simplifies the process.

Deep Learning Engineers are specialised software developers responsible for the research, design, and development of self-running programs to automate predictive models. Their role is to create artificial intelligence (AI) systems that utilise large datasets to generate and construct learning and forecasting algorithms. In order to construct high-performance machine learning models, the Deep Learning Engineer must analyse, evaluate, and structure data, perform tests, and improve the learning process.

If you are passionate about data, automation, and algorithms, a career in machine learning may be the perfect fit for you. You will have the opportunity to work with large amounts of raw data, develop algorithms to process and analyse the data, and then automate the process to ensure maximum efficiency.

What does Deep Learning engineering entail?

Deep learning engineering roles are currently highly sought-after across a range of industries, providing career stability and a broad range of opportunities. A number of projections indicate that the global Artificial Intelligence (AI) and Machine Learning (ML) market is set to grow steadily from 2018 to 2027. Market research company IDC forecasts that the global AI market will be worth over $500 billion by 2024.

The increasing worldwide interest in artificial intelligence and machine learning technologies and applications has resulted in a dramatic growth of AI startups, as well as an increase in existing companies’ interest in the field. Between 2015 and 2018, the number of AI startup acquisitions almost quadrupled, and since 2010, this number has nearly tripled. This rise in acquisitions of AI businesses has been in tandem with the funding for AI startups, which has risen from over one billion dollars in 2013 to an impressive 8.5 billion dollars in the first quarter of 2020.

What are the duties and tasks of a Deep Learning engineer?

Deep learning engineer duties on the team include a variety of responsibilities, such as –

• Backend infrastructure, data pipelines, and/or machine learning models will be created for an AI-powered service.
• Ranking models are being developed in order to automate and develop modelling procedures.
• Assist in the creation of new features that address complex data management issues.
• End-users are provided with and tested machine learning models.
• Combine computer science principles such as data structures, algorithms, and machine learning to create amazing ML models.
• This course covers, among other things, programming languages, distributed systems, and information retrieval.

In addition to the core responsibilities of a deep learning engineer, they may be called upon to carry out additional tasks, depending on the particular needs of the organisation. As the field of deep learning is still in its infancy, organisations are likely to have unique approaches to leverage this technology for maximum productivity. Therefore, each organisation may have different requirements for deep learning engineers.

As a consequence, deep learning engineer jobs in IT organisations may encompass a variety of extra responsibilities, such as:

• Collaboration between data scientists and business analysts.
• Infrastructure automation.
• Creating APIs from machine learning models.
• Putting AI and machine learning models through their paces before deploying them.
• Machine learning is being used to generate minimum viable products.
• AI is being used to provide fresh talent to enterprises.

How does one go about becoming a Deep Learning engineer?

To be a successful deep learning engineer, one must possess certain qualifications. This role entails designing and developing high-functioning machine learning models by gathering and analysing data, carrying out tests and experiments, and continuously assessing and optimising the learning process.

As a Deep Learning Engineer, you will be tasked with utilising algorithms to a variety of codebases, so prior experience in software development is highly advantageous. In order to be successful in this role, a good understanding of mathematics, statistics, and web programming is essential. Once these concepts have been mastered, you will be well-equipped to pursue a career in Deep Learning Engineering.

Deep Learning engineers must have certain skills

The field of Deep Learning Engineering is rapidly growing, and there is no single path to becoming a Deep Learning Engineer. Depending on your educational goals, technical proficiency, and areas of interest, there are various ways to enter this sector. Artificial Intelligence and Machine Learning have already transformed the Information Technology, Financial Technology, Healthcare, Education, Transportation, and other industries, and this trend is only expected to continue in the future. Organisations are recognising the potential of AI, making the shift from experimentation to implementation, and investing in AI and ML capabilities as quickly as possible. Hence, Deep Learning Engineer jobs are likely to see an increased demand in the upcoming years.

To advance your career in the United States, you will need to gain the following skills:

1. Knowledge of software engineering

   Deep learning engineers must be proficient in a range of computer science fundamentals in order to be successful in their roles. These fundamentals include the ability to write algorithms that can search, sort, and optimise, as well as an understanding of data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays. Additionally, knowledge of computability and complexity, as well as an understanding of computer architecture, including memory, clusters, bandwidth, deadlocks, and cache, is essential.

2. Data science knowledge

   Deep learning engineers require a comprehensive understanding of data science fundamentals, including programming languages (e.g. Python, SQL, and Java), hypothesis testing, data modelling, mathematics, probability, and statistics (e.g. Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, etc.). Additionally, they must possess the ability to develop an evaluation strategy for predictive models and algorithms.

3. Expertise in machine learning

   Machine Learning Engineers possess a variety of skills in the areas of deep learning, dynamic programming, neural network designs, natural language processing, audio/video processing, reinforcement learning, sophisticated signal processing techniques, and machine learning algorithm optimisation. These skills enable Machine Learning Engineers to develop and deploy advanced machine learning models that can solve complex problems.

4. AI/ML systems place a high value on security

   In order to ensure the success of Machine Learning models, it is imperative that data preparation is conducted thoroughly. Moreover, access to data should only be granted to personnel and applications that have been provided with authorization. Data security is a critical skill that must be learnt at all costs.

5. Real-world project experience is preferred

   As an ML Engineer, it is essential to have a clear understanding of when and how to leverage your technical expertise for practical applications. Demonstrating your ability to complete an Artificial Intelligence/Machine Learning development project from inception to completion, with detailed documentation, is a great way to showcase your skills and experience to potential employers. This will help you acquire the remote ML Engineer roles that you have been aiming for.

6. Communication skills

   As a Deep Learning Engineer, it is essential to be able to effectively communicate project objectives, timetables, and expectations to stakeholders. This often requires working collaboratively with Data Scientists and Analysts, Software Engineers, Research Scientists, Marketing Teams, and Product Teams. To ensure the success of the project, it is important to be able to effectively explain the details of the project to these stakeholders, in a way that they can understand and appreciate.

7. Problem-solving skills

   Deep learning engineers must possess the critical and creative thinking skills necessary to address problems in real-time. This requires an aptitude for problem-solving, a trait which is also necessary for data scientists and software engineers. Therefore, being able to solve problems is a fundamental requirement for any professional working in the field of deep learning.

8. Subject matter expertise

   As a deep learning engineer, it is essential to possess knowledge of both the company’s needs and the types of challenges that their designs are meant to address. This understanding is necessary in order to create self-running software and to optimise solutions for both businesses and customers. Without a comprehensive understanding of the domain, a machine learning engineer’s proposed solutions may be inaccurate, their work may overlook important characteristics, and analysing a model may prove to be a difficult task.

How can I get work as a remote Deep Learning engineer?

Deep learning engineers must take the initiative to update their knowledge with the latest breakthroughs in their field and continuously expand their skills in order to remain competitive. In order to be successful, they must also ensure that they are adhering to the best practices in their industry. Two things that developers should consider as they move forward in this regard are seeking assistance from a more experienced and trained teacher to help them acquire new skills, and honing their analytical, computer programming, artificial intelligence, and machine learning skills. As such, designers should make sure that they have access to support to help them achieve their goals.

At Works, we provide the most exceptional deep learning engineer job opportunities to help you achieve your career objectives in Artificial Intelligence and Machine Learning Engineering. Our positions enable you to work with the most innovative technology while tackling complex technical and business problems, allowing you to rapidly progress in your profession. Additionally, you can join a prestigious circle of the world’s top developers to find full-time, long-term remote deep learning engineering roles with higher salaries and accelerated career advancement.

Job Description

Responsibilities at work

• Constructing back-end infrastructure, data pipelines, and/or deep learning models for AI-powered products
• Using essential coding abilities, improve current deep learning systems.
• Take complete control of deep learning systems.
• Create features and large-scale recommendation systems.
• Determine fresh chances to apply deep learning to various aspects of the product.
• Introduce new functionality to address difficult data management issues.
• Create operational ranking models and automate modelling workflows.

Requirements

• Bachelor’s/degree Master’s in engineering, computer science, or information technology (or equivalent experience)
• At least three years of experience as a deep learning engineer is required (rare exceptions for highly skilled developers)
• AI, deep learning, and machine learning technology expertise
• Excellent mathematical and analytical abilities
• Understanding and application of data science concepts
• Knowledge of Python, Matlab, Linux, and C++ is required.
• To communicate successfully, you must be fluent in English.
• Work full-time (40 hours per week) with a 4-hour overlap with US time zones

Preferred skills

• Front-end technology knowledge and deployment
• Strong knowledge of cloud computing platforms such as AWS, Azure, GCP, and others.
• Knowledge with user interface technologies such as Django and Flask