The use “Machine Learning” has accelerated considerably over the last few years. Collecting and extracting hidden business information from large amounts of data pays off – and businesses understand that.
Machine Learning engineers play an important role in helping business organizations extract insights from a variety of data types, generate business value and make informed decisions. Global enterprises like Amazon and Netflix have been leveraging machine learning solutions for customer personalization and drive their recommendation engines, ultimately using machine learning to grow and achieve desired their businesses.
But how to hire Machine learning engineers that are qualified and can fit your project requirements well? And what are the factors that can validate the professionalism of your potential Machine Learning engineers? Learn more about how to achieve your Machine learning development goals in this article.
What Makes an Experienced Machine Learning Engineer?
The key role of a Machine learning engineer is to focus on designing, building, and deploying machine learning models and help businesses leverage gained insights.
However, to find Machine learning engineers that can apply a comprehensive approach you have to outline selection criteria. So, let’s take a closer look at the characteristics that define qualified Machine Learning engineers.
Strong Technical Foundation
The tech stack of your Machine learning engineers may vary according to your project requirements and project complexity. Nevertheless, to help you narrow the qualified candidates, here are a general list of expertise that every qualified machine learning engineer should possess:
- Knowledge of certain major programming languages. A good command of Python, C++, or R is one of the key essentials when looking for well-versed Machine learning engineers.
- Strong Machine Learning expertise. The proficiency areas of Machine learning engineers should span basic concepts, including unsupervised and supervised learning, ML model optimization, neural networks, and more.
- Experience in building Machine Learning Models: a good ML engineer should know how to develop and train models that can make predictions or automate decision-making processes based on data.
- Extensive Data Analysis Experience: Machine learning engineers must be adept at extracting actionable insights from data and integrating those insights into business strategies.
- Familiarity with Data Visualization Tools: Competence in tools like Tableau is essential for translating data into visual formats that inform and support decision-making.
- Command of Advanced Statistical Methods: Skills in areas such as pattern recognition and predictive modeling are critical to success in machine learning roles.
Recognized professional certifications
You might ask how to hire a Machine learning engineer with proven analytical skills among a myriad of specialists. Look for experts that have successfully passed one or more data science accreditations, such as Microsoft Certified Azure Machine learning engineer Associate, AWS, and others. These credentials validate their expertise across several key areas of data science and machine learning.
Industry-Specific knowledge
When assembling your machine learning team, it’s vital to hire engineers who have experience in your industry or a closely related field. These engineers are more likely to understand the business impact of technical decisions and effectively communicate data insights to stakeholders. Industry expertise enhances their ability to tailor solutions that align with your business goals.
Seniority Level
The expertise of your machine learning engineers is directly linked to the quality of their work. Mid-level and senior engineers typically possess a deeper understanding of best practices and have extensive experience working with complex, unstructured data sets.
You should also assess the track record of potential hires. Engineers with three or more years of experience in research and analytics are likely to deliver better project outcomes. At Trustify Technology, for example, our mid-level machine-learning engineers average three to five years of experience, while our senior engineers have six years or more in the field.
By focusing on these key characteristics, you can ensure you are hiring skilled machine learning engineers who can deliver impactful results for your business.
How to Hire Machine Learning Engineers Who Can Deliver Results?
When hiring skilled machine learning engineers from an external software vendor, choosing a trustworthy machine learning outsourcing partner can be a daunting task, especially with so many vendors available. To help you make an informed decision, we’ve outlined the key factors to consider when selecting a machine learning solutions provider:
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Clear understanding of your business goals
Begin your research for Machine learning engineer vendors by defining your business request. For example, do you need to personalize customer experience or implement models for predictive analysis to control system failures? Depending on your needs, you can now narrow the list of your potential Machine Learning Development partners by comparing their featured services and projects’ outcomes.
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Outsource to Countries with large Machine Learning talent pool
To save you time and costs, we recommend preparing a list of outsourcing destinations with significant numbers of Machine learning engineers for hire. You are more likely to find experts with relevant experience and tech stack in locations that boast large talent pools.
In these terms, Asia is one of the most promising global destinations to consider. According to LinkedIn, Vietnam is home to more than 1000+ Machine learning engineers. Other Asian countries with great reputations are Singapore and Indonesia being home to over 4600 Machine learning engineers.
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Company size
If you want to hire Machine learning engineers promptly and with no hindrances, opt for mid-sized to large software engineering companies with 100+ tech experts on board. These vendors usually offer access to many Machine learning engineers for hire, along with optimized development processes.
Major enterprises have larger internal recruitment databases that ensure quick project starts. In addition, those companies tend to follow the best industry practices and put much focus on quality control.
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Significant domain expertise
You want to find Machine learning engineers that would be able to turn data into actionable insights and help you get ahead of competitors in your industry. Make sure that your potential partner has experience in a domain related to yours. A portfolio of several similar projects can guarantee that the company’s ML experts will be able to approach machine learning development effectively with considerations to all the industry peculiarities.
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Intellectual Property and Data Protection policy
When it comes to data collection and processing, security is of the utmost importance. It is essential that your outsourcing vendor protects sensitive information by adhering to international standards such as GDPR, HIPAA, CCPA, and PCI DSS, etc. Don’t forget to ask if your partner has a Business Continuity and Disaster Recovery Plan to rely on if any incident occurs.
Another sign of a reliable software engineering company to hire Machine learning engineers is the implementation of best practices for secure development. For machine learning engineers, these practices include encrypting data traffic, ensuring data reproducibility, and implementing secure data handling techniques.
Side-Notes
Contact Trustify Technology today and see how we can help you select the right Machine Learning engineers for your project.