AI/ML/Data Engineers

Hire AI/ML/Data Engineers

Data Engineering is a wide subject that includes many different topics. It enables continuous data flow in a data-driven world by focusing on developing trustworthy infrastructures. These professionals act as conduits for clean and raw data from multiple sources, enabling employees to utilize it to make data-driven choices inside the company.

Similarly, AI and ML are among the most intriguing technical advancements we’ve ever seen. Their applications may be found in a variety of fields. When we don’t know where we’re going, AI guides us, allows smart, safe parallel parking, and proposes new online goods that meet our requirements. You could even use AI to schedule meetings or choose a new favorite TV program to watch.

AI is always growing, and in the next decades, we will witness more AI-powered voice assistants managing our everyday tasks.

As a result, the need for remote AI/ML/Data engineer employment is growing. There has never been a more interesting or better moment to work as an AI/ML/Data developer. But first, learn more about what they do and what it’s like to work in this industry.

What are the opportunities in AI/ML/Data engineering?

AI/ML/Data engineering is already having an influence on our future, and there is a growing need for experienced engineers. AI and machine learning seem to hold the key to improving specialized human tasks including voice recognition, image processing, business process management, and illness identification.

Since these technologies are being employed in a variety of sectors throughout the globe, including healthcare and education, job opportunities have increased at an exponential pace. Similarly, data engineering is a discipline that assists businesses in making data-driven choices. The rising popularity and uses of these technologies offer a bright future for developers working for remote AI/ML/Data engineer employment.

What are an AI/ML/Data engineer’s tasks and responsibilities?

AI/ML/Data engineers build AI models from the ground up to help businesses make vital choices. They develop systems that can be taught to anticipate future occurrences, solve issues, and provide solutions. As an AI/ML/Data engineer, you will be in charge of a variety of activities, such as designing and testing algorithms, utilizing tools such as R, and delivering your final products to customers.

The following are the primary tasks of a developer after obtaining remote AI/ML/Data engineer employment.

  • Convert machine learning models into application programming interfaces (APIs) so that they may be used by other systems.
  • Create AI models from the ground up and assist various segments of the firm in understanding the model’s outcomes.
  • Organize the configuration of the data science team.
  • Create infrastructure for data input and transformation.
  • Perform statistical analysis and fine-tune the data so that the company can make better decisions.
  • Create and manage artificial intelligence product and development infrastructure.

How does one go about becoming an AI/ML/Data engineer?

You may start or grow your AI/ML/Data engineering career with the proper mix of skills and experience. AI/ML/Data engineers often have a bachelor’s degree in computer science, engineering, applied mathematics, or a related IT subject. Prospective AI/ML/Data engineers may discover that a boot camp or certification is inadequate since the task needs a high degree of technical understanding.

A degree may help you establish a firm foundation of knowledge in an ever-changing sector. A master’s degree may also assist you progress your career by allowing you to apply for higher-paying remote AI/ML/Data engineer employment.

SQL database design experience and programming abilities in a variety of languages, including Python and Java, are required. A boot camp or certification might help you generate a decent CV for remote AI/ML/Data engineer employment if you already have a background in IT or a related subject like mathematics or analytics.

If you have no past expertise with technology or IT, you may need to take a more intensive program to show your comprehension. If you don’t already have one, you may want to consider enrolling in an undergraduate degree. If you have a bachelor’s degree but aren’t working in a related industry, consider master’s degrees in data analytics and data engineering.

If you spend some time looking through job advertisements, you’ll have a better feel of how your expertise fits into that employment function.

Qualifications for becoming an AI/ML/Data engineer

Begin by studying all of the fundamental skills required for remote AI/ML/Data engineer employment. Let’s go through the essential abilities right now.

  1. Python

    Working remotely for AI/ML/Data engineer positions necessitates dealing with massive volumes of data that must be evaluated fast. Python’s simple syntax makes it simple to learn and code in. They may guarantee that elements in complex systems are linked explicitly. One of the primary reasons Python is used for AI is the vast number of libraries available. Python libraries offer basic objects so that developers do not have to create them from scratch every time. Continuous data processing is required for AI, and Python’s modules allow you to access, analyze, and update data.
  2. Java

    Java is a platform-agnostic programming language that can run on any machine. Because all of the platform-specific information is included in a single package, its Virtual Machine Technology enables you to create programs and rapidly deploy them everywhere. AI programming in Java includes machine learning, genetic algorithms, search algorithms, and neural networks.
  3. C++

    C++ is a fantastic programming language that AI/ML/Data developers utilize. It provides a variety of programming tools and library functions, making it an excellent choice for dealing with complex problems. C++ is a multi-paradigm programming language that adheres to object-oriented principles and is useful for data organizing.
  4. LISP

    LISP is another useful programming language for obtaining remote AI/ML/Data engineer positions. It is a computer language family and the second oldest programming language after Fortran. LISP has grown over time into a sophisticated and dynamic coding language. LISP is used in AI because of its adaptability in prototyping and testing fast. It is a programming language that adapts to the needs of the developer and solves particular issues successfully.
  5. Big data and Spark technologies

    AI/ML/Data engineering specialists deal with huge volumes of data on a daily basis, akin to what you’d find in a digital library. To make sense of all this data, they’ll need access to big data technologies like MongoDB and Cassandra. As AI software progresses, more algorithms are needed to analyze data in real-time.
  6. Frameworks and Algorithms

    Every aspiring AI/ML/Data engineer must first understand machine learning methods such as linear regression, KNN, Naive Bayes, Support Vector Machine, and others before trying to develop machine learning models with simplicity. Furthermore, while dealing with unstructured data such as images and videos, one should be knowledgeable with deep learning algorithms and how to develop them using a framework. To get remote AI/ML/Data engineer employment, you must first grasp the key algorithms and frameworks.
  7. Hadoop

    The Apache Hadoop software library is a framework that uses basic programming concepts to allow the distributed processing of massive data volumes across clusters of devices. It is intended to grow from a single server to tens of thousands of devices, each with its own processing and storage capacity. Many programming languages are supported by the framework, including Python, Scala, Java, and R. While Hadoop is the most powerful technology for handling enormous amounts of data, it does have certain downsides, such as delayed processing and a high degree of coding.

How can I get work as a remote AI/ML/Data engineer?

Every profession need a unique set of talents and personality qualities. A developer should constantly be on the lookout for new opportunities to put their skills to use. To accomplish the task with entire sincerity and devotion, the desire to learn new things, innovate, and welcome errors is also required.

Works provides the top remote AI/ML/Data engineer jobs that align with your career goals as an AI/ML/Data engineer. In 2022, further your career with Works by working on hard technical issues with cutting-edge technology. Join a network of the world’s greatest developers to find full-time, long-term remote AI/ML/Data engineer jobs with greater pay and opportunities for advancement.

Job Description

Responsibilities at work

  • Create AI/ML products with excellent user experiences.
  • Understand the project, create an effective plan, and oversee the whole project lifetime.
  • Design and create highly scalable models/classifiers/algorithms using ML/AI concepts.
  • Work with cross-functional teams to communicate and influence technical specifics of projects.
  • Integrate and ship code into the cloud environment on a regular basis.
  • Produce wire-frame mock-ups in collaboration with product managers.
  • Drive usability sessions to fine-tune procedures, get buy-in, and progress toward full deployment.


  • 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)
  • Competent in any general-purpose programming/query language, including Python, SQL, PHP, Java, and C#.
  • English fluency is required for collaboration with engineering management.
  • The opportunity to work full-time (40 hours a week) and a 4-hour time zone overlap with the United States.

Preferred skills

  • Understanding of huge systems, complicated code bases, and version control systems such as Git.
  • Expertise in ML libraries, predictive modeling, pattern recognition, data mining, and other areas.
  • Knowledge of common data science toolkits such as R, NumPy, MatLab, and others.
  • Experience with machine learning frameworks and libraries (such as Keras and PyTorch) (like Scikit-learn, NLTK)
  • Expertise in applied statistics, such as regression, distributions, and statistical testing.
  • In-depth knowledge of Artificial Neural Networks and Deep Learning Frameworks.
  • Full-stack (FE & BE Development), Distributed & Parallel systems, and so on.
  • Excellent knowledge of algorithms, data structures, and computer science principles.
  • Function and discover effective answers to challenges without being micromanaged.