One of the sub-domains of Artificial Intelligence (AI), Machine Learning (ML), trains the machine to self-learn from experience. The technology is evolving at a rapid pace and is now highly popular among businesses as it helps to make classifications and predictions and discover key insights. This subsequently leads to better decision-making, ideally impacting key growth metrics.
Overall, we can say that hiring ML engineers help companies build applications that can learn from past user patterns or behaviors. Thus, it’s not solely learning but listening, understanding and learning by imitating the way humans learn but also gradually improving its accuracy.
The technology is also finding its growing use with mobile applications as it helps increase business sales by offering recommendations based on users’ previous purchases or interests. These advantages have fueled a massive growth of ML and, in turn, machine learning engineers. If you are also looking to add ML to your tech stack, we are here with a complete guide to hiring talented developers skilled in machine learning and related technologies.
An ML engineer focuses on researching, creating, and designing self-learning AI systems. They generally work as a part of a team of data science and communicate with data scientists, administrators, data analysts, data engineers, and data architects depending on project needs. Sometimes, they may also need to communicate with external teams depending on the project requirements.
Many confuse machine learning engineers with data scientists. While the roles are similar in the sense that they include handling large amounts of data and using similar technologies. But ML engineers majorly focus on creating and managing self-running AI systems and predictive models. Data scientists, on the contrary, extract meaningful insights from large data sets.
As big data continues to grow, the market demand for data scientists and ML professionals is also increasing incessantly. Where data scientists identify the most relevant business pain points and the data to answer them, ML engineers’ major focus is to research, build and design artificial intelligence (AI) systems to automate predictive models.
However, as the supply of specialized talent is still scanty, hiring a good ML engineer or a skilled data scientist remains a demanding task. This is not only because of the lack of ML talent but also the lack of relevant experience among the hiring specialists.
And, if you are also interested in bringing an ML experience to your app or are trying to build a new one, the below roles and responsibilities will ensure that you recruit the right machine learning engineer.
While this may vary between organizations, some common roles, and responsibilities are as follows:
- Design of machine learning systems
- Researching and implementing ML algorithms and tools
- Studying and transforming data science prototypes
- Ability to select the best data sets
- Ability to select the best data representation models
- Can verify data quality
- Know how to run ML tests
- Can expand ML libraries and frameworks
- Able to fulfill client requirements in making ML apps
- Able to retrain ML systems when needed
- Can use results to improve ML models
- Can perform statistical analysis
- Willingness to remain updated with new developments in the field.
For hiring junior or senior-level engineers, you can use the following strategies
General job boards: They are effective but can often attract applications from non-qualifying candidates.
Specialized job boards: Offer high-quality candidates already working in this domain.
University tie-ups: To hire junior ML staff, this can be an efficient option.
Competitions or hackathons: Can attract junior ML engineers and data scientists.
Recruitment platforms: While for hiring experienced ML engineers you can look for specialized job boards, they may not be appropriate to hire highly qualified specialists. In that case, you can take assistance from recruitment platforms that provide pre-vetted ML engineers quickly and without any hassle.
ML conferences: Conferences and meetups are powerful tools for finding high-quality ML talent. Here you can build your network with ML professionals.
To hire a professional engineer who can develop an app matching your business requirements, look for the following skills.
An ML engineer should be aware of frameworks, libraries, and other ML technologies. Plus, the developers should have both a software engineering degree and relevant experience in data science. An experience in coding, tools, and software is also required.
Some programming languages and frameworks where you should test their expertise include:
Python: A candidate should know this programming language as it is fundamental to ML algorithms.
Pytorch/TensorFlow: A candidate should have hands-on experience with these two machine learning technologies as they dominate the market.
Scikit-learn: Having command over this library can solve many data problems and helps in predictive data analysis.
NumPy and Pandas: The candidate should know how to use NumPy for basic functionality and Pandas for complex ones.
Apache Spark: ML engineers should be aware of Apache Spark to speed up development efforts.
The developer should be able to communicate clearly and concisely. For this, you can connect to them through some communication software. For a more structured approach, you can try implementing an approach where they need to connect to clients on particular days or times through audio or video call. You can do the same between you two as well.
An ML engineer must have great cognitive abilities, which means they should be innovative enough to align the app with your business purpose and make it stand out from the competitors.
As technology is ever-changing with new developments, ML engineers should have the zeal to learn, understand and implement new technologies. Additionally, they should be aware of the latest ML trends and be able to implement them in existing apps to increase ROI.
The developer should have relevant experience working in ML-based applications. This should reflect in the interview or the written test you conduct to access the developer skill.
The ML engineer you are planning to hire should also be experienced in data science and its various aspects. This includes methods, algorithms, processes, and knowing how to handle structured and unstructured data to make it suitable for business requirements.
Software and mobile app development has seen an unprecedented improvement after its integration with ML technologies. Now applications are much more advanced in that they learn from their own mistakes to improve the user experience. This makes hiring an ML engineer vital for any business. So, if you are planning to hire a machine learning engineer, ensure the developer has strong technical expertise. You can check his communication skills, portfolio of previous works, data science experience, and time commitment.