Machine learning (ML) is an important branch of artificial intelligence that is making a huge impact globally. Experts believe that ML will “drive economic growth in every industry” over the coming decades.
Since machine learning systems “learn” from huge data sets to improve the accuracy of the “insights” that they extrapolate, ML has many use cases. Examples include natural language processing (NLP), speech recognition, computer vision, image recognition, image processing, etc.
Many industries such as banking, financial services, manufacturing, healthcare, etc. stand to benefit enormously from ML. This is why many organizations are already using ML to augment the processes powering their existing systems.
Whereas in the past they might have already used automation for simple tasks, they are increasingly managing more and more complex tasks with the help of the ML’s data-driven decision-making capabilities. Examples include stock trading, supply chain management, and, of course, chatbots.
These factors make ML engineers highly sought-after in the job market. As such, you might find it hard to hire ML engineers due to this soaring demand.
The good news is that you have options at hand to hire smart ML engineers. However, you need to choose the right hiring platform so that you get started on the right foot. The main two types of platforms are the freelancer platform and the dedicated software development company that outsources its full-time machine learning developers.
If you want to save time and get straight to hiring the best then why not send DevTeam.Space your project requirements and one of our dedicated account managers will get in touch to answer any questions that you might have. This will save you a lot of time and effort while ensuring that you get the best developers who have expertise in your specific industry.
Before we analyze the pros and cons of each type of platform, we will first review the skills required from professionals undertaking machine learning engineer jobs. Note: The skills you will need will depend on your unique project requirements. This is a general list.
Primary skills required in a machine learning engineer role
You should look for ML engineers with these primary machine-learning skills:
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- Proficiency in any one of the programming languages like Python, Julia, and Java;
- Good knowledge of useful ML development libraries offered by the leading programming languages, e.g., Numpy or Scikit-learn in Python;
- Thorough understanding of types of ML algorithms, e.g., supervised and unsupervised learning, etc.;
- Deep knowledge of predictive algorithms;
- Thorough knowledge of machine learning algorithms like Naïve Bayes classifier algorithm, K-means clustering algorithms, Support Vector Machine (SVM) algorithm, Linear Regression, Logistic Regression, Artificial Neural Networks (ANNs), Decision Trees, Random Forests, Nearest Neighbors, Dimensionality Reduction, Gradient Boosting, Principal Component Analysis (PCA), etc.;
- Sound knowledge of linear algebra;
- Familiarity with tools and techniques used to process unstructured data;
- Good knowledge of preparing data sets from raw data, e.g., data cleaning, data analysis, etc.;
- Experience with AI models;
- Knowledge of ML model deployment processes;
- Good knowledge of ML and AI development platforms like Microsoft Azure Machine Learning Platform, Google AI Platform, etc.
Note: Mid-level ML engineers should have the above skills. If you need senior-level ML engineers, then the skill requirements are larger still.
Advanced skills required in senior-level machine learning engineering jobs
Experienced ML developers need the following skills:
- Sound knowledge of one or more AI capabilities like deep learning, neural networks, computer vision, image recognition, speech recognition, natural language processing, etc.;
- Experience in the data science field;
- Working knowledge of creating data science models;
- Data modeling skills;
- Proficiency with machine learning frameworks;
- In-depth understanding of statistical analysis;
- Sound knowledge of big data technologies like Hadoop, Apache Spark, etc.;
- Familiarity with deep learning models;
- Understanding of how to deploy deep learning models;
- Software development experience using programming algorithms offered by the relevant languages;
- Data engineering skills;
- Experience in developing predictive algorithms;
- Familiarity with tools used by data scientists and data analysts;
- Knowledge of how to design self-learning software systems;
- Experience in developing a machine learning algorithm;
- Knowledge of tuning machine learning models;
- Knowledge of the methodologies and practices in data science teams;
- Understanding of how data science teams use ML algorithms and models.
Senior ML developers should have an understanding of how data engineers work. They might need knowledge of programming languages, such as R, that are used in data science projects.
Other software engineering skills required by machine learning engineers
You need smart ML developers to have the following software engineering skills:
- Sound knowledge of software engineering;
- Sufficient familiarity with software development lifecycle (SDLC);
- Good understanding of software development methodologies like Waterfall and Agile;
- Extensive experience in computer architecture;
- Knowledge of ensuring that software performs in line with the non-functional requirements (NFRs) like performance, scalability, etc.;
- Experience in developing secure applications by using tools like encryption;
- API development skills;
- Familiarity with human-centered design;
- Extensive code review experience;
- Deep knowledge of defect management and software quality management;
- Experience in using the leading version-control tools;
- Knowledge of how software engineers can use cloud computing to their advantage;
- Familiarity with planning and executing tests;
- Sufficient experience with DevOps processes, methods, tools, and practices.
Note: Larger data science teams and ML development teams use cloud computing platforms to meet their project’s computing power requirements. Software engineers in ML or data science projects also use the DevOps tools offered by these cloud platforms. Make sure you have expertise in cloud computing as part of your core development team.
Competencies required in machine learning engineer jobs
In order to have a successful machine learning career, ML engineers need the following competencies:
- Customer focus;
- The ability to see the larger perspective;
- The urge to excel;
- Communication skills;
- Problem-solving skills;
- Collaboration skills;
How to find the top-ranked machine learning engineers?
Now that you know about the general skills required in a machine learning developer, start the hiring process. Take the following steps:
1. Choose the right platform to hire ML engineers
ML is closely related to AI, furthermore, data scientists routinely use it. AI solutions like virtual assistants, AI-based trading, AI-powered risk assessment, etc. make considerable use of ML. Machine learning engineers design crucial solutions for data science prototypes too. We highlight all of these to point out the importance of ML.
The right hiring platform makes a big difference to any project. You might think that you can get ML engineers at a low hourly rate via a freelance platform. However, we don’t recommend this approach.
Freelance developers work part-time on your project since they must juggle projects to ensure a constant income. You might not get enough focus from them. Managing freelancers in different time zones can be hard, furthermore, freelance platforms offer any project management support.
Freelancers might leave your project mid-way if offered something better. If they do, you will be back to square one as you need to spend more of time and effort hiring replacement developers.
We recommend you hire full-time ML engineers from a trustworthy software development company like DevTeam.Space. We have highly skilled and experienced ML developers who work for our platform on a full-time basis.
In addition to our robust vetting process, we routinely encourage our developers to upskill. Therefore, our developers are highly motivated. We also provide management support.
Alternatively, if you require, we can outsource you a cohesive ML development team or simply undertake the full software development cycle in-house while you concentrate on other parts of the project.
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2. Interview the candidates
After you select a hiring platform, you need to post your job advert. Make sure that this nails all the skills that you need from your ML developer.
Once you get applications, you will need to interview your short-listed candidates.
Interviewing an ML engineer will require expertise in multiple technologies like Python, SQL, Hadoop, etc. Do you need interview questions? Use our comprehensive interview questions. Check out the following examples:
Top Tip: We highly recommend having a senior ML developer on hand to accurately assess the skills of your ML developers. We always follow up with ‘ping pong’ questions where we bat back their original answer with a new question that probes their skills even deeper. This is an excellent way to find the best ML developer.
Tailor your interview process to assess relevant skills and experience. E.g., evaluate the experience of candidates on key aspects like data structures, data processing, ML model performance, data distribution, etc.
As previously stated, additional job machine learning engineer responsibilities include collaborating with stakeholders like customers, project managers, architects, testers, DevOps engineers, etc. Therefore, assess the experience of candidates working in complex project environments to make sure they are up to the task.
Describe your project requirements. Ask how the candidate will approach your project.
3. Onboard ML engineers effectively
Assuming that you found and hired competent ML developers, it’s time to onboard them. Focus on making the onboarding process lean and efficient. This helps ML engineers to become productive quickly.
Provide the project documents to your new developers. Explain your project requirements to them. You might need to explain the technical solutions and software architecture too.
Provide access to the project’s technical environment. The new ML developers need access to the code repository and other relevant tools.
You might already have existing software development teams, e.g., web development teams. Introduce the new developers to your existing team. Explain the roles and responsibilities of the team members.
Describe the project plan, furthermore, explain the project milestones. Establish a communication process with your new developers. You need to describe the work approval process. Finally, you need to establish accountability.
Tips for interviewing ML developers
The following interviewing tips could help you:
A. Look for the right balance of experience
Machine learning projects can be complex. Hire sufficiently experienced developers. You need the right mix of senior and mid-level developers in your team. Get in touch if you need help with this.
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B. Evaluate the experience of implementing ML algorithms
ML algorithms are numerous. You need to implement the most suitable algorithms for your project’s requirements. ML engineers with experience in ML libraries in programming languages like Python can effectively implement various algorithms. Assess their skills in this regard.
C. Look for developers with relevant experience
ML is a vast and evolving branch of AI. New innovations routinely emerge. Even the most experienced ML engineers might not know about all corners of this vast field. Look for developers with the specific capabilities that you need.
Samples of machine learning engineering questions
As previously noted, the best way to find a good developer is to have an even better developer to interview them. It is vital that you have this expert involved in the interview process. Reach out if you need help.
Ask questions to evaluate relevant and hands-on skills. Check out these examples:
A. Describe how you used cloud computing to make ML development more effective
This is an open-ended question. Expect candidates to describe how they used cloud computing to meet computing power-related needs. They should also talk about how they used the DevOps tools.
B. Describe a deep learning problem you solved.
In response to this open-ended question, candidates should describe the project problem. They should explain the different deep learning-based alternatives they explored. Candidates should explain why they chose one over the rest. They should explain the implementation process.
C. Explain how you have used labeled and unlabeled data while implementing ML algorithms
This is an open-ended question. Expect the candidates to describe how they have used supervised, unsupervised, and semi-supervised ML learning algorithms for appropriate data sets.
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If you are looking for expert ML engineers, DevTeam.Space is a great place to start. We have a large community of field expert ML developers and can connect you to the most suitable one for your project.
You are more than welcome to contact us by filling out a DevTeam.Space product specification form to let us briefly know about your project. After filling out the form, one of our dedicated account managers will get back to you to answer any questions you might have.