Remote AI/ML engineer jobs

AI-ML engineers build, test, and deploy AI models, maintain the underlying AI infrastructure, as well as navigate between traditional software development and machine learning implementations.

AI-ML engineers focus on researching, planning, and producing self-running Artificial Intelligence systems to automate predictive models. AI-ML engineering jobs responsibilities also include designing and developing the AI algorithms competent of learning and making predictions that explain Machine Learning. It allows engineers not to rely on a series of steps, but learn from the data supplied into ML algorithms.

Remote AI-ML engineering jobs are seeing an exponential increase as more and more companies worldwide lean towards automation. If you have gained expertise and fine-tuned your AI-ML skills, you can become a top AI-ML engineer. AI-ML engineering offers the opportunity to bag a secure, high-paying remote job.

AI-ML engineering jobs offer career stability and various opportunities due to their high demand across industries. This profession has seen an exponential rise in job listings by over 300% between 2015 and 2018. And this number continues to grow as more and more organizations worldwide accomplish the potential of coupling big data with software.

While Artificial Intelligence is an umbrella term with various applications, obtaining the skills and specializing in particular areas take time and maturity. Rather than anything, prospective careers would necessitate a desire to be interested and take risks.

As different industries are always in demand for highly-skilled AI-ML engineers, AI-ML engineering jobs listings are rarely empty. These engineers are the best problem solvers who create, test, and execute numerous AI models. They are also involved in the creation and management of self-operating applications that promote ML projects.

AI-ML engineer’s responsibilities on the team include multifarious tasks, like -

• Writing a Machine Learning algorithm to capture the whiteboard sketches of website layouts drawn by the UX team in order to produce finished website layouts for the Software development team. If executed successfully, this workflow will help companies save a lot on man-hours, and accelerate the feedback loops related to website UX improvements
• Accumulating data from multiple HotJar users and driving it against ML algorithms to find frequent pitfalls and roots to user distraction. With the right data analysis, companies can find user distraction patterns like how, when, and why.
• Creating a model connecting HotJar and A/B testing data with the Google Analytics data and information. It will help in creating improved layouts, which will raise the time spent on the site, higher customer acquisition, etc.
• Predicting the success of various layouts recommended by the UX team.

Besides these, there might be more to the role and responsibility of an AI-ML engineer. As this field is still very young and many things are yet to be identified, each business has some specific implementations of productive automation practices.

Hence, AI-ML engineering jobs may have many further responsibilities in IT organizations, like:

• Coordination between data scientists and business analysts
• Infrastructure Automation
• Machine Learning models transformation into APIs
• Test and deploy AI-ML models
• Development of minimum viable products based on ML
• Utilization of AI to empower businesses with new skills

Let's move on to the track, which is inescapable to pursue a professional career with the AI-ML engineering jobs. To start, keep in mind, you need to be formally educated with a bachelor’s or master’s degree in mathematics, statistics, computer science, data science, or any relevant subject to become an AI-ML engineer. Other than this, you also need command of the relevant technical and non-technical skills. Fresher AI-ML engineers may get jobs in start-ups and small businesses where they will work in multiple areas of AI-ML engineering.

However, you may have heard that to get remote AI-ML engineering jobs, you must have 3-5 years of experience. It is true for a couple of reasons.

• First, industry experience allows you to recognize the vast opportunities while working remotely in top Silicon Valley companies.
• Second, many organizations hire candidates with a proven track record to ensure a risk-free, fruitful hire.

In light of the above points, you should always keep an AI-ML engineer resume handy with you.

Now, let's look at the skills and practices you'll need to understand to join the league of remote AI-ML engineers.

The AI-ML engineering jobs sector is a relatively new and constantly evolving field. Because of this, there is no hard and fast skill set defined to become an AI-ML engineer. There are different scopes to get into the sector depending on your educational background, technical skills, and areas of interest. AI and ML are already reshaping industries like IT, FinTech, healthcare, education, transport, etc., and these have a long way to go. Organizations are shifting more to AI value, getting out of the experimentation phase, and focusing on expediting AI-ML adoption. Therefore, AI-ML engineering jobs will be more in demand in the near future.

If you want to push your career with an elite U.S. job, the seven skills you need to master:

1. Programming languages

The very first skill AI-ML engineers need to grow is their experience with multiple programming languages. According to GitHub, the top 10 machine learning languages include - Python, C++, JavaScript, Java, C#, Julia, Shell, R, TypeScript, and Scala. While Python is the most popular programming language, Scala is becoming popular in certain areas, like interacting with big data frameworks, such as Apache Spark.

2. Data engineering (ETL)

For AI-ML system development, one crucial stage is pre-processing and storage of raw data that is generated by the systems. Every time when new data is generated, the AI-ML engineer needs to create ETL (Extract, Transform, Load) pipelines to process, cleanse and store data to make it easily accessible by other processes, such as analytics and predictions. AI-ML engineers need to recognize data models and connect the resolutions from data science with the fundamentals of software engineering.

3. Data analysis

It is an essential skill for AI-Ml engineers to be able to conduct experimental data analysis on a dataset to recognize unusual patterns in data, define specific aberrations, and analyze hypotheses. To bag the best AI-ML engineering jobs, you should be able to create summary statistics for a dataset, generate graphical representations that allow for easy data visualization, clean and prepare data for modeling, perform feature engineering to obtain more information from the dataset, etc. to improve the ML models you will develop.

4. Models

To become a pro in AI-ML engineering, you need to be exceptionally skilled in machine learning algorithms, as well as know when to implement those. Further, to do more complex tasks, like image classification, object detection, face recognition, machine translation, dialogue generation, etc., you need to have a good grasp of complex algorithms that are based on artificial neural networks.

5. Services

Once you establish the most relevant machine learning model for solving a given problem, next you need to decide whether to implement the model from scratch or use existing services. If you need to develop new ML models and a fully managed platform that allows you to promptly and easily build, train and deploy those into a production-ready hosted environment, mastering in AWS SageMaker will be a great plus.

6. Security

As in every software solution, managing security for AI-ML solutions is a crucial task. While the Machine Learning models need a lot of data preparation, data accessibility should be given to the authorized people and applications only. Data security is an exceptionally crucial skill to master.

7. Experience with real-world projects

Another vital part of becoming an AI-ML engineer is recognizing where to apply your technical knowledge to actual tasks and assignments. Completing an AI-Ml engineering project end-to-end and documenting it in your portfolio will help you promote your skills and understanding to future employers.

AI-ML engineers need to work hard enough to stay updated with all the recent advancements in the AI-ML field and grow their skills gradually over time. In order to excel at their profession, they need to follow the best practices effectively and consistently. In this regard, there are two factors to consider for the engineers to focus on to progress. They might need to find support from someone who is more experienced and effective in training new techniques while they are practicing. Further, as an AI-ML engineer, it's vital to fine-tune the analysis, computer engineering, and artificial intelligence, and machine learning skills. So, the engineers need to make sure there is someone who will help them out and keep an eye on their progress.

Turing offers the best remote AI-ML engineering jobs that suit your career trajectories as an AI-ML engineer. Grow rapidly by working on challenging technical and business problems on the latest technologies. Join a network of the world's best developers & get full-time, long-term remote AI-ML engineering jobs with better compensation and rapid career growth.

Every AI/ML engineer at Turing has the freedom to select his/her rate. Turing, on the other hand, will recommend a wage at which we are confident we can offer you a rewarding and long-term opportunity. Our remote AI/ML engineer jobs recommendations are based on our market analysis and demand from our most prestigious clients.