Machine Learning is a subset of Artificial Intelligence(AI) that helps to turn data into insights and insights into actionable strategies.
Personalized social media experiences, product recommendations tailored to user needs, and automatic fraud detection are some examples of the impact that Machine Learning has today.
With more than half of international organizations keeping their Artificial Intelligence and Machine Learning initiatives as a high priority, there is increasing demand for Machine Learning Engineers.
This article provides insights and strategies to hire Machine Learning Engineers the right way.
What Is a Machine Learning Engineer?
A Machine Learning Engineer, also known as ML Engineer, focuses on designing and building AI algorithms that learn from data and make informed predictions.
They work within a data science team alongside data professionals such as Data Scientists and Data Engineers. Furthermore, they have a background in computer science, statistics, and mathematics.
What Does a Machine Learning Engineer Do? Roles & Responsibilities
A Machine Learning Engineer acts as a bridge between data science and implementation of algorithms. They help to translate the insights from the Data Scientists into working Machine Learning models. Here are the key Machine Learning Engineer responsibilities:
- Designing and developing Machine Learning algorithms
- Selecting the right data sets for training purposes
- Fine-tuning models using test results
- Ensuring high data quality to perform statistical analysis
- Training Machine Learning models
- Utilizing relevant frameworks and libraries within Machine Learning
- Researching the use-cases for the ML systems and prioritizing high impact ones
- Developing Machine Learning integrated apps as per user needs
- Staying updated on latest trends in ML and AI and experimenting with new algorithms
Machine Learning Engineer vs Data Scientist
A Data Scientist focuses on identifying and validating key pain points of the business that can be solved utilizing Machine Learning.
On the other hand, an ML Engineer is more focused on deploying these ML models into production.
Data Scientists are focused on analyzing the data and creating custom algorithms. But, ML Engineers focus on optimizing the models. Data Scientists focus on tools that optimize the modeling workflow but ML Engineers focus on optimizing the deployment workflow.
The Machine Learning Engineer vs Data Scientist interrogation is further clarified by the key skills that each possesses.
An ML Engineer is focused on software engineering, Machine Learning frameworks, data structures, and data modeling. On the contrary, Data Scientists focus on data analytics, data visualizations, and problem-solving.
There are some overlapping skills such as programming, with both roles involving the use of Python, Github, and tools for metadata storage such as Neptune.ai.
However, Data Scientists have expertise in R and SQL along with frequent use of Jupyter Notebook and libraries such as Scikit-learn. Conversely, ML Engineers are adept at C++, Scala, Linux, and Tensorflow.
Machine Learning Use Cases: How Is Machine Learning Used
Executives around the world are currently asking the question on how Machine Learning can be used to improve their internal processes and develop a competitive edge. Here are some of the most popular Machine Learning use cases.
Through the use of Machine Learning and natural language processing(NLP), AI Chatbots are an upgrade from rigidly programmed chatbots.
These new chatbots enabled by AI can understand the context of the requests better and provide conversational answers mimicking humans.
2. Dynamic Pricing
Businesses have been able to maximize their profits by utilizing dynamic pricing to capitalize on the real-time changes in supply and demand in the market.
Delta Airlines, one of the largest airlines in the U.S. created ML algorithms to make real-time updates to their airline prices based on demand for the tickets as well as availability of similar flights by airlines.
3. Recommendation Engines
Among the use cases of Machine Learning, recommendation engines are one of the most common because of their effectiveness in increasing sales and customer retention.
These engines are built based on a customer’s purchase history along with complex algorithms to recommend relevant products and services the customers are most likely to buy.
The online book marketplace Goodreads has created a recommendation system that recommends books to users based on their profile of previous purchases and user behavior on their website.
4. Fraud Detection
A key threat to the financial industry and its customers is fraud which can lead to severe damage to a company’s reputation.
Machine Learning is used to detect any outliers that could indicate fraudulent activity by creating models that measure which transactions fall within or outside a normal range for a customer.
5. Process Optimization
As per Harvard Business Review, 31% of respondents in their survey said that using Machine Learning and AI helped to increase their revenue and market share.
Such companies achieved these results by optimizing their existing processes which led to speeding up of work and reduction of human error.
6. Decision Support Systems (DSS)
Machine Learning is also helping workers to make the best decisions. This is done by predicting future trends, identifying issues before they require critical attention, and helping speed up the decision-making process.
For example, the healthcare industry has been using decision support systems to assist doctors in diagnosing patients faster and with higher accuracy.
7. Predictive Maintenance
Companies can save costs and increase their production by utilizing predictive maintenance. This includes manufacturers, airlines, logistics, and mining companies.
Predicting the optimal time to perform maintenance on equipment is possible by combining data from different parts of a business such as data from operations, IoT devices in the equipment, and the supply chain processes.
8. Customer Churn Modeling and Customer Segmentation
Among Machine Learning use cases, 57% of applications are focused on improving customer experience.
Machine Learning Engineers can use techniques such as logistic regression and decision trees to forecast customers who are most likely to churn.
Furthermore, by clustering customers based on a range of behavioral patterns, businesses can create customized offerings or run marketing campaigns based on such customer segmentation.
9. Sentiment Analysis
Sentiment analysis uses Machine Learning models to interpret human emotions and rate them. This is especially helpful for brands to measure how their users are reacting to their marketing schemes and campaigns.
For instance, Nike regularly uses sentiment analysis for their social media campaigns by analyzing tweets, related news, and purchase intent.
10. Cyberthreat Detection
A nightmare scenario for companies today is cyberthreats which can leave their customers’ data vulnerable along with millions of dollars in fines.
Machine Learning Engineers use anomaly detection, behavioral analysis, pattern recognition, and real-time monitoring to ensure that any unusual activity is detected and acted upon.
Benefits of Machine Learning in Business
The Machine Learning market is expected to grow to $225 billion by 2030 rising by 36% year over year until then. The clear value proposition and high return on investment in Machine Learning are driving this rapid growth.
Here are some of the benefits of Machine Learning in business.
1. Retain Customers by Analyzing Historical Data
One of the standard tactics to maximize revenue is by retaining existing customers.
Investing in Machine Learning capabilities helps to make the most out of the historical data that a company has developed over time.
The right systems can help to optimize the customer experience and minimize customer churn increasing profitability.
2. Eliminate Unplanned Downtime Through Predictive Maintenance
Using Machine Learning helps to identify equipment that is likely to experience downtime. This helps to save costs as these repairs can be scheduled accordingly.
Such an advantage is why 93% of manufacturing companies believe that using AI will be a key growth driver for them.
3. Launch Recommender Systems to Maximize Revenue
Creating recommender systems helps to create cross-selling and upselling opportunities for businesses.
Its effectiveness in maximizing revenue is why companies such as Spotify, Amazon, Netflix, and Uber Eats have invested heavily in these systems.
4. Enhance Strategic Planning and Forecasting
A key part of any business is predicting future outcomes and planning accordingly.
Utilizing Machine Learning models helps to create data-driven predictions which can help forecast demand for products, manage inventory, and improve product availability based on seasonality.
5. Evaluate Patterns to Identify Fraudulent Activities
Businesses can minimize financial losses and reputational damage by detecting signs of fraud with early warning systems.
By using Machine Learning algorithms that thoroughly identify any anomalies, businesses can adhere to compliance standards better and provide assured data integrity to their clients.
6. Enhance Operational Efficiency and Cut Costs
Lowering costs while increasing efficiency is among the key advantages of Machine Learning. A subset of Machine Learning, Generative AI has made huge strides that will significantly reduce costs.
McKinsey has predicted that current Generative AI technologies can help to automate work activities that currently take up to 70% of an employee’s time.
Key Skills to Look Out for When Hiring Machine Learning Engineers
It’s important to hire a Machine Learning Engineer only after carefully assessing their skills and competencies since the wrong hire may lead to inadequate results for the project.
Here are the key Machine Learning Engineer skills to evaluate.
1. Technical Skills
The primary proficiency to measure to hire Machine Learning Engineers is technical skills since their role revolves around getting Machine Learning models into production. Some of the major technical skills to assess are:
- Programming Proficiency: Expertise in Python or Java is necessary along with an understanding of data structures.
- Machine Learning Algorithms and Frameworks: Ask ML Engineers about deep learning architectures and specific Machine Learning algorithms. Additionally, evaluate their ability with TensorFlow, and PyTorch.
- Software Engineering Principles: Implementing ML models requires understanding how testing, debugging, and modularity work.
- Machine Learning System Design: Having a comprehensive understanding of the details of how to build an end-to-end Machine Learning infrastructure is essential. Additionally, examine how familiar they are with the tools to create Machine Learning pipelines.
- Distributed Computing: This is among the most crucial Machine Learning Engineer technical skills as it helps to deal with large datasets and computationally intensive tasks. To analyze the candidate’s ability, ask them about their experience with Hadoop and Spark.
- Cloud Computing: The majority of Machine Learning models are deployed in cloud platforms today so its great for the Engineers to be familiar with AWS, Azure, or Google Cloud Platform.
- Specific Frameworks: Additionally, ML Engineers need to be acquainted with Keras, MXNet, XGBoost, and LightGBM.
2. Communication Skills
Machine Learning Engineer skills need a balance of soft skills in today’s workplace. The most important among soft skills is the ability to communicate with clarity, especially to non-technical audiences.
Hire a Machine Learning Engineer who can work as part of a team and has a helpful attitude. Inquire about their previous experience working within a team and focus on seeing if they have a collaborative mindset.
Problem Solving is among the non-negotiable Machine Learning Engineer required skills as candidates who lack it might have difficulty in getting to the root cause of problems and developing the right solutions.
Common Mistakes to Avoid When Hiring Machine Learning Engineers
While there may be a rush to find the right Machine Learning Engineer as quickly as possible, not thinking through the process can lead to the wrong hire instead. Here are common pitfalls to avoid before considering a Machine Learning Engineer for hire:
- Neglecting Soft Skills: Given that the role of an ML Engineer is highly technical, its common for recruiters to fail to look for soft skills. This may lead to candidates who are hard to work with and cause conflicts within the team.
- Ignoring Practical Experience: Machine Learning Engineers with an educational background in the field but lacking the necessary experience may take much longer to start producing results.
- Overlooking Portfolio and Projects: Its necessary to analyze the previous projects of the candidates and have them run through how they successfully executed them to assess their actual competency.
- Underestimating Business Acumen: Talents who work with a siloed mentality that overlooks actual business outcomes may not bring effective results.
- Neglecting Cultural Fit: Consider asking candidates about their values and working style. For instance, a talent who is habituated to working in a casual setting might have difficulty working with an organization with a more structured culture.
How to Hire a Machine Learning Engineer: Choosing the Perfect Candidate
The demand for AI and ML professionals is set to grow by 40% from 2023 to 2027. It is thus necessary to have the right process for hiring such talents, with the steps below.
1. Initial Screening
It’s a recommended practice to conduct an initial screening to filter candidates with potential based on the specific requirements for the position.
2. Technical Assessment
Design a technical assessment that includes a technical interview along with case studies to know what is the skill level of the candidate. Furthermore, think about whether a hands-on coding exercise would be necessary.
3. Project Compatibility Check
Once there is a set of talents who are technically adept, it is also important to see if they align with the specific project of the company along with a cultural fit.
4. Trial Integration
To be completely sure about whether the talent is the right Machine Learning Engineer for the organization, it requires working with them. Start with a short-term paid project that will help to provide assurance on their ability and fit.
5. Final Onboarding
If the talent is the right fit then move towards finalizing the hire by providing a compensation package that will incentivize them to stick at the company for the long-term.
How Much Does It Cost To Hire a Machine Learning Engineer?
The Machine Learning Engineer cost varies by geographic location. A case in point, the average annual salary for an ML Engineer in the U.S. is $150,000 while in Canada it is $73,000.
The costs are lower in Poland, at $50,000 per year, and Ukraine, at $30,000 per year.
Best Models for Hiring Machine Learning Engineers
There are generally three models for hiring talents which are highly dependent on the specific requirements of a business. They include:
The in-House model means bringing in a full-time employee who becomes an integrated part of the team.
- They become an integrated team member providing full attention to complex Machine Learning projects
- Better communication and coordination within the team
- The recruitment process can take from weeks to months
- The wrong hire may lead to costly monthly salary and benefits
Freelancers are independent contractors who work on a project-by-project basis.
- More affordable as payment is done on a project basis
- They might bring in new skills and expertise within Machine Learning that isn’t present in the internal team
- Many freelancers juggle multiple projects which may lead to coordination issues
- There may be data security concerns to bring in a freelance Machine Learning Engineer
Outsourcing means working together with a third-party recruitment firm that takes care of the entire recruitment process.
- Speedy recruitment process as they specialize in this field
- Quick access to pool of talent in low-supply areas such as Machine Learning
- There may be communication issues between the two teams
- Providing vague requirements may result in a mismatch of talent expectation.
The growth in remote work has created an opportunity for businesses to access quality talent from around the world.
Hire Offshore Machine Learning Engineers
With the rising costs of hiring tech talent in countries such as the U.S. and Canada, businesses have been considering alternative destinations with equally competent talent at lower costs.
This trend is set to continue as 16% of companies are already fully remote, operating without a physical office.
Why Hire Machine Learning Engineers from Poland or Ukraine?
Poland and Ukraine have emerged as optimal locations for tech talent, including Machine Learning Engineers. Some of the key reasons ML Engineers from Poland or Ukraine include:
- Skilled Workforce: There is a large pool of skilled tech talent in these countries who have a demonstrated history of working with cutting-edge innovations in Machine Learning.
- Cost-Effective Talent: In comparison to countries in North America and Western Europe, the cost for talent is lower though the quality of work is high.
- Strong Educational Background: Both Poland and Ukraine have a large number of universities that focus on a rigorous tech curriculum.
- Thriving Tech Communities: The presence of many Fortune 500 companies and homegrown tech startups has led to a growing Machine Learning ecosystem within these countries.
- Fluency in English: Language barriers are one of the key roadblocks to working with global talents. Thankfully, Poland and Ukraine have very high English-speaking rates.
Let RemoDevs Help You Hire Machine Learning Engineers
RemoDevs specializes in hiring the best tech talents from Poland and Ukraine. We can help you hire Machine Learning Engineers who fit your offer and company culture by taking care of the first part of the recruitment process.
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