AI/ML Engineers

Hire AI/ML Engineers

AI-ML engineers are responsible for designing, constructing, and deploying Artificial Intelligence (AI) and Machine Learning (ML) models. In addition to this, they are tasked with managing the supporting infrastructure and transitioning between traditional software development and ML implementations. This role requires a combination of technical and problem-solving skills in order to create and test models that meet the customer’s requirements.

AI-ML engineers specialise in researching, constructing, and maintaining self-operating Artificial Intelligence systems to automate predictive models. This type of engineering job requires inventing and creating AI algorithms that are capable of acquiring knowledge and generating predictions to interpret Machine Learning. AI-ML engineers are also able to learn from the data that is fed into their ML algorithms, allowing them to find insights that would otherwise be missed if they were only following a predetermined set of instructions.

As the popularity of Artificial Intelligence and Machine Learning technology continues to grow, more and more businesses are turning to automation to increase efficiency. This has led to a rise in remote AI-ML engineering jobs, offering potential candidates the opportunity to pursue a secure and highly-lucrative career from the comfort of their own home. For those who have acquired the necessary knowledge and honed their AI-ML skills, this presents an exciting chance to become a leading AI-ML engineer.

What does AI/ML engineering entail?

Due to the increasing demand for Artificial Intelligence and Machine Learning engineering roles across various industries, such positions offer a more secure career path with a diverse range of opportunities. Between 2015 and 2018, job postings for this profession experienced an enormous spike of over 300%, and this number is anticipated to grow even further as more businesses around the world recognise the advantages of combining large-scale datasets with computer programming.

While Artificial Intelligence (AI) is an expansive term encompassing a wide range of applications, developing proficiency and focusing on specific areas requires significant time and dedication. Prospective AI-related jobs will necessitate an eagerness to get involved and take risks more than anything else.

Due to the ever-increasing demand for highly skilled Artificial Intelligence (AI) and Machine Learning (ML) engineers, job opportunities in this area are rarely left unfilled. These engineers are the best of the best when it comes to problem-solving, as they are involved in the creation, testing, and implementation of various AI models. Additionally, they are responsible for the development and management of applications designed to support ML initiatives.

What are the duties and functions of AI/ML engineers?

The team’s AI-ML engineer is responsible for a variety of activities, including –

• Developing a Machine Learning algorithm to automatically capture the UX team’s whiteboard drawings of website layouts and generate the final website layouts for the Software development team could be a valuable solution for businesses. This approach could save a considerable amount of time and expedite the feedback loops associated with changes concerning the website user experience. If implemented correctly, this technology could drastically reduce the amount of time and resources needed to make timely changes.
• By analysing the data gathered from numerous HotJar users, companies can use Machine Learning algorithms to identify common issues and sources of user distraction. Through careful data analysis, it is possible to uncover user distraction patterns, including the timing, frequency and potential causes of distraction.
• By developing a model that integrates HotJar and A/B testing results with Google Analytics data and metadata, we can create more effective page layouts that will help to increase user engagement, customer acquisition, and other key performance indicators. This model will provide valuable insight into how our users interact with our website, allowing us to make informed decisions about web design and development.
• Predicting the success of different UX team-recommended layouts.

In addition to the aforementioned duties, AI-ML engineers may be expected to carry out additional tasks related to their role. As the field of AI-ML is relatively new, each organisation has its own unique set of automated methods that can be used to maximise efficiency and profitability. As such, engineers must be prepared to adapt to different approaches to ensure the best results for their employer.

As a result, AI-ML engineering roles in IT firms may include a variety of additional tasks, such as:

• Collaboration between data scientists and business analysts
• Automation of Infrastructure
• APIs are created by transforming machine learning models.
• AI-ML models should be tested and deployed.
• Minimum viable products based on machine learning
• AI application could provide firms with new talents

How do I become an AI/ML engineer?

In order to pursue a successful career in AI-ML engineering, it is essential to obtain a bachelor’s or master’s degree in mathematics, statistics, computer science, data science, or a related field. Additionally, it is important to possess both technical and non-technical skills. Fresh AI-ML engineers can seek out opportunities with start-ups and small businesses, where they can work on a wide range of AI-ML engineering projects.

However, you may have heard that you need 3-5 years of experience to secure remote AI-ML engineering positions. It is correct for many reasons.

• For starters, industry knowledge enables you to understand the many chances available when working remotely at top Silicon Valley firms.
• Second, to assure a risk-free, profitable recruitment, many firms choose applicants with a demonstrated track record.

Given the above, you should always maintain an AI-ML engineer resume on available.

Let’s take a look at the abilities and techniques you’ll need to know in order to join the ranks of remote AI-ML developers.

Qualifications for becoming an AI/ML engineer

Artificial Intelligence (AI) and Machine Learning (ML) engineering jobs are a relatively new and fast-growing field. As such, there is no single set of skills required to become an AI-ML engineer, and the entry path can vary depending on one’s educational background, technical expertise, and areas of interest. AI and ML are already making an impact in industries such as Information Technology, Financial Technology, healthcare, education, and transportation, and there is still plenty of potential for growth in these areas. Companies are starting to focus more on the value that AI can bring to their operations, transitioning from the testing phase to a focus on accelerating AI-ML implementation. As a result, AI-ML engineering roles are expected to become increasingly sought after in the coming years.

If you want to further your career with an exceptional U.S. employment, these are the seven talents you must master:

1. Computer programming languages

   Aspiring Artificial Intelligence and Machine Learning (AI-ML) developers must be well-versed in a variety of programming languages to be successful. According to GitHub, the top ten machine learning languages include Python, C++, JavaScript, Java, C#, Julia, Shell, R, TypeScript, and Scala. Python is the most popular language, and is widely used by developers. However, Scala is becoming increasingly popular due to its ability to interface with big data frameworks, such as Apache Spark. It is essential that AI-ML developers are knowledgeable in a range of languages to stay ahead of the curve.

2. Information engineering (ETL)

   One of the key components in the construction of Artificial Intelligence (AI) and Machine Learning (ML) systems is the pre-processing and preservation of their produced raw data. As additional data is generated, it is essential for the AI-ML engineer to generate Extract, Transform, Load (ETL) systems to process, clean, and store the data in a manner that it can be quickly accessed by other processes such as analytics and forecasting. In order to achieve this, AI-ML engineers must recognise data models and combine data science techniques with software engineering principles.

3. Data examination

   In order to uncover previously unknown patterns in data, identify exceptional trends, and evaluate postulations, AI-ML engineers need to be able to conduct exploratory data analysis on a dataset. Demonstrating proficiency in this area is essential for securing top AI-ML engineering roles. Such proficiency includes being able to generate summary statistics for a dataset, create graphical representations that promote data comprehension, clean and organise data to make it suitable for modelling, and perform feature engineering to extract more knowledge from the dataset.

4. Models

   In order to become an AI-ML engineering expert, it is essential to have a deep understanding of machine learning algorithms and when to use them. Additionally, to be able to complete more intricate tasks such as image classification, object detection, face recognition, machine translation, and dialogue synthesis, it is important to have a thorough knowledge of advanced algorithms based on artificial neural networks.

5. Providers of services

   Once the most suitable machine learning model has been identified for a particular problem, it is necessary to decide whether to create this model from the ground up or taking advantage of existing services. If constructing new ML models is essential, and there is a need for a managed platform to quickly and competently build, train and deploy them into a production environment, then having an understanding of Amazon Web Services SageMaker will be highly beneficial.

6. Security

   Ensuring the security of Artificial Intelligence (AI) and Machine Learning (ML) systems is of utmost importance, just like any other software solution. Developing ML models requires a substantial amount of data preparation, and it is essential that only authenticated users and applications are granted access to the data. Consequently, it is essential to learn and master the art of data security.

7. Real-world project experience

   Gaining an understanding of how to apply your technical knowledge of Artificial Intelligence and Machine Learning to real-world tasks and assignments is a key element of becoming an AI-ML engineer. Demonstrating the ability to complete an AI-ML engineering project from its conception to its execution, and ultimately documenting it in your portfolio, will be a great way to showcase your skills and knowledge to potential employers.

How can I find remote AI/ML engineer jobs?

As an AI-ML engineer, it is essential to remain abreast of the latest breakthroughs in the AI-ML sector and to consistently develop and refine your skills. To succeed in this field, you must adhere to the best practices and be willing to invest time in learning and practice. To help you reach the next level, it can be beneficial to have access to someone more experienced in the field who can provide guidance and support. Additionally, it is important to invest in sharpening your analytical, computer engineering, artificial intelligence and machine learning abilities. To ensure steady progress, it is important to have someone available to monitor your progress and provide assistance when needed.

At Works, we offer the most sought-after remote Artificial Intelligence and Machine Learning engineering jobs that are tailored to match your career aspirations as an AI-ML engineer. We provide you with the opportunity to build and refine your skills quickly by tackling complicated technical and commercial problems with the most up-to-date technology. Additionally, you are invited to join a global network of the most talented developers to find full-time, long-term remote AI-ML engineering jobs that offer higher salaries and an accelerated career progression.

Job Description

Responsibilities at work

• Improve the performance of existing AI applications; improve existing ML modules and frameworks
• Create an effective plan and manage the full project lifecycle as needed.
• Apply statistical analysis to complicated data-set challenges.
• Machine learning algorithms must be trained and tested.
• Create highly scalable deep learning systems based on AI and ML concepts.
• Influence the technical components of the projects to effectively communicate technical information.
• Integrate and ship code into the cloud environment on a regular basis.
• Produce wire-frame mock-ups in collaboration with product managers.

Requirements

• Computer Science Bachelor’s/Degree Master’s (or equivalent experience)
• 3+ years of expertise in AI, ML, Deep Learning, or Natural Language
• Processing is required (exceptions for highly skilled devs)
• Knowledge of one or more programming languages, such as Python, Java, and others.
• Experience with topics such as un/supervised learning, database modelling, and so on.
• English fluency is required for collaboration with engineering management.
• The capacity to work full-time (40 hours per week) with a four-hour overlap with United States time zones.

Preferred skills

• Command of complicated code bases, big systems, and version control systems such as Git
• Knowledge of ML libraries, predictive modelling, pattern recognition, data mining, and so on
• Knowledge of popular data science toolkits such as NumPy, Pandas, Matplotlib, NLTK, and others.
• Knowledge of programming languages like as R, MATLAB, and others.
• Knowledge of machine learning frameworks (like Keras or PyTorch)
• Expertise in applied statistics, such as regression, distributions, and statistical testing.
• Comprehensive knowledge of Artificial Neural Networks and Deep Learning Frameworks
• Excellent knowledge of algorithms, data structures, and computer science principles