![Andile Ngcaba](./img/client-review/andile-ngcaba.jpg)
Andile Ngcaba
Chairman at Convergence Partners Investments
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ML Engineer
9+ years experience • Full-time availability
Verified Skills
Other Skills
ML Engineer
7+ years experience • Full-time availability
Verified Skills
Other Skills
ML Engineer
8+ years experience • Full-time availability
Verified Skills
Other Skills
ML Engineer
4+ years experience • Full-time availability
Verified Skills
Other Skills
ML Engineer
6+ years experience • Full-time availability
Verified Skills
Other Skills
ML Engineer
9+ years experience • Full-time availability
Verified Skills
Other Skills
From food to FinTech, thousands of companies use Supersourcing to hire, scale and grow faster.
Machine learning engineers can develop models to analyze medical images and identify potential health issues, such as tumors or other abnormalities. They can also develop models to predict patient outcomes and assist in the development of new drugs and treatments.
Machine learning engineers can develop models to detect fraud, predict stock prices, and analyze customer behavior to identify potential risks or opportunities.
Machine learning engineers can develop models to recommend products to customers, predict demand for products, and optimize pricing and inventory management.
Machine learning engineers can develop models to optimize production processes, predict equipment failures, and improve the efficiency of supply chains.
Machine learning engineers can develop models to optimize logistics, predict traffic patterns, and improve the safety and efficiency of transportation systems.
Machine learning engineers can develop models to optimize crop yields, predict weather patterns, and improve the efficiency of farming operations. Marketing: Machine learning engineers can develop models to predict customer behavior, identify potential sales opportunities, and optimize marketing campaigns.
Can't find the answer you are looking for?
The entire process takes around 2-10 days. A clear job description and fast interview turnarounds can reduce this duration.
Supersourcing takes the responsibility of managing employees timesheet, availability. One Senior Account manager will be assigned to each project. We don't prefer bot on support. Our senior team is available even in weekends to support you in your business. Just an Email/WhatsApp away.
Firstly, we understand their technical knowledge through Mettl & HackerEarth. Secondly, we manually verify all data points through different sources to ensure the highest quality of talent.
We don't work with freelancers. We work with developers who are looking for full-time work but at different organisations. The verification interview is also done to ensure seamless compatibility with different companies.
Monthly to yearly, we have different options that companies can choose from.
We assign every company an account manager. Please do reach out to your point of contact to add and remove developers as per requirement.
Yes you can hire them on permanent basis, after 6 months of contact pay one fixed finding fees and hire them on your payroll, Try before you buy. We are really flexible depends on your need.
We recently started in Metro cities in India and Globally; Check with sales team for feasibility! So far we deployed only 700 engineers at location.
Supersourcing will match you with senior developers that fit your JD within 5 days. Sometimes, our expert team can match profiles in even less than a day.
Other Platforms Vs Supersourcing
Multiple Job Boards
You sign-up & subscribe to multiple job boards.
Intelligent Hiring Platform
You sign-up & share your JD highlighting skills, experience, responsibilities.
Wasting Time Interviewing Unskilled Talent
Scouring through multiple resumes & interviewing multiple candidates, wasting time & resources.
AI will Find the Best 5 Matches
100% Profile Matching ensures you need only one round of interview to select the best among the 5.
Waiting for Acceptance
Once you send the offer letter, there is no guarantee that they will accept, delaying your project.
Get Started Immediately
Team Supersourcing will take care of onboarding, timesheets, productivity reports, & post-hiring support.
Traditional Sourcing- Hiring is Slow, Costly & Risky
Time Spent
30 Days
Hiring Cost
$30,000+
Supersourcing - Tech-Driven, Risk-Free, Futuristic
Less than a week
5
Days
Zero upfront cost
$0
Machine Learning Engineers
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence that uses statistical techniques to enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so.
In machine learning, a model is trained on a data set. This training data is used to teach the model to identify patterns and relationships in the data. Once the model is trained, it can be used to make predictions or decisions about new data, without the need for human intervention. There are several types of machine learning:
This type of machine learning is used for classification and regression tasks. The model is trained on labeled data, where the correct output is provided for each input.
This type of machine learning is used for clustering and dimensionality reduction tasks. The model is trained on unlabeled data, and the goal is to identify patterns and structure in the data.
This type of machine learning is used for decision making and control tasks. The model learns to make decisions based on feedback from its environment.
This type of machine learning is a combination of supervised and unsupervised learning, where the model is trained on a dataset that includes both labeled and unlabeled data.
Machine learning is widely used in various industries such as healthcare, finance, transportation, and manufacturing, to improve operations, increase efficiency, and make better data-driven decisions.
Businesses should hire machine learning engineers because they have the skills and expertise to design, develop, and deploy machine learning models and systems that can analyze and make predictions or decisions from data.
Machine Learning engineers also have the ability to select appropriate algorithms and technologies for specific use cases and evaluate and improve the performance of models over time. They are able to take the raw data and turn it into actionable insights and predictions. They also design and implement the infrastructure and tools needed to build, test, and deploy these models, which is critical for the successful implementation of machine learning in a business setting. They can also create and maintain data pipelines, data warehousing and data governance, which are essential for the smooth functioning of any machine learning system. In short, hire machine learning engineers as they play a critical role in making machine learning a practical and valuable tool for businesses to improve their operations, increase efficiency and make better data-driven decisions.
Shortlisting and hiring machine learning engineers can be a complex process, as there are many factors to consider when assessing a candidate's qualifications and experience. Here are some tips to help you shortlist a machine learning engineer:
Look for candidates who have a degree in a field related to computer science, mathematics, or statistics. A master's or PhD degree in a related field is a plus.
Look for candidates who have a solid background in machine learning and have worked on projects that are relevant to your business.
Look for candidates to hire Machine Learning engineers who have a strong understanding of machine learning algorithms and tools, such as Python, TensorFlow, and scikit-learn. Also, check if they have experience with big data technologies such as Hadoop and Spark.
A good machine learning engineer should have experience in deploying models to production, and have knowledge of various cloud platforms such as AWS, Azure and GCP.
Look for candidates who have a strong ability to analyze and solve problems, and who have experience working with large and complex data sets.
Look for candidates who have a strong understanding of data science concepts such as data pre-processing, feature engineering, and data visualization.
If your business requires deep learning experience, look for candidates who have knowledge of deep learning frameworks such as PyTorch and Keras.
Look for candidates who have experience working with other related skills like computer vision, natural language processing, and time series analysis.
Machine learning engineers need to be able to communicate their findings and recommendations to non-technical stakeholders, so look for candidates who have strong communication skills.
Finally, conduct in-person or virtual interviews with the candidates to evaluate their understanding of machine learning and their ability to communicate their thought process clearly.
By following these tips, you can shortlist and hire Machine Learning engineers who are well-suited to your business needs and who have the skills and experience to help your organization succeed.
A Machine Learning engineer typically needs to have skills in the following areas:
The cost to hire machine learning engineers can vary greatly depending on factors such as location, experience, and skill level.
In the United States, the average salary for a Machine Learning engineer is around $120,000 per year. However, experienced Machine Learning engineers with specialized skills and a strong track record can command much higher salaries, with some earning upwards of $200,000 or more.
In India, the average salary of a Machine Learning Engineer is around 8-12 LPA (Lakh per annum)
The cost of hiring a Machine Learning engineer can also vary depending on the type of hiring arrangement. For example, hiring a full-time employee will typically be more expensive than hiring a contractor or freelancer on a project basis.
Machine Learning engineers are responsible for preparing and cleaning data sets to be used in machine learning models. This includes tasks such as handling missing data, removing outliers, and transforming data into a format that can be used by machine learning algorithms.
Machine Learning engineers are responsible for creating new features from raw data that will improve the performance of machine learning models. This includes tasks such as creating new variables, combining existing variables, and selecting relevant features for a given model.
Machine Learning engineers are responsible for selecting the appropriate machine learning algorithm for a given problem, and for evaluating the performance of different models. This includes tasks such as tuning model hyperparameters, comparing different algorithms, and choosing the best model based on performance metrics.
Machine Learning engineers are responsible for training machine learning models using data and deploying them to production. This includes tasks such as defining the training pipeline, monitoring the training process, and fine-tuning models to improve performance.
Machine Learning engineers are responsible for monitoring the performance of deployed models, and for making updates and adjustments as necessary. This includes tasks such as monitoring model performance metrics, identifying and fixing bugs, and retraining models as needed.
Machine Learning engineers are responsible for creating visualizations and reports that help stakeholders understand the performance of machine learning models. This includes tasks such as creating interactive dashboards, generating performance metrics, and creating visualizations that help stakeholders understand model results.
Machine Learning engineers are responsible for working with cross-functional teams and communicating their findings to stakeholders. This includes tasks such as working with data scientists, software engineers, and domain experts, and communicating findings in a clear and concise manner.
Machine Learning engineers are responsible for continuously improving the performance of models. This includes tasks such as researching new algorithms, implementing new techniques, and experimenting with different approaches to improve performance.
A Machine Learning engineer should have a strong background in computer science, mathematics, and statistics. They should have experience with programming languages such as Python and machine learning libraries such as TensorFlow, PyTorch, and scikit-learn. They should also have a strong understanding of machine learning algorithms and concepts, as well as experience with data preprocessing, cleaning, and feature engineering.
A Machine Learning engineer should have experience with building, deploying, and maintaining machine learning models in a production environment. They should also have experience with data preprocessing, cleaning, and feature engineering, as well as experience with machine learning libraries such as TensorFlow, PyTorch, and scikit-learn.
The role of a Machine Learning engineer is to design, build, and deploy machine learning models that solve real-world problems. They are responsible for selecting the appropriate machine learning algorithm for a given problem, and for evaluating the performance of different models. They also play an important role in maintaining and updating deployed models, and in working with cross-functional teams to communicate their findings to stakeholders.
Some common challenges when hiring Machine Learning engineers include finding candidates with the right qualifications and experience, and assessing their abilities in a short amount of time. Additionally, Machine Learning engineers are in high demand, so there may be competition for top talent. Finally, the field of machine learning is constantly evolving, so staying up-to-date on the latest trends and developments can be a challenge.