Machine Learning (ML) Salary in India | How Much Does an ML Engineer Earn
Who is a Machine Learning Engineer?
A Machine Learning Engineer is an avid programmer who helps machines understand and pick up knowledge as required. Their core deliverables include creating programs that enable machines to take specific actions without explicit directions.
Apart from programming, Machine Learning engineers are also responsible for customising data sets for analysis, personalising web experiences, identifying and predicting business requirements. This role also demands exceptional communication skills since they often collaborate with other teams to drive different optimisation projects. Hiring companies typically look for candidates with a master’s degree and a few years of experience in similar roles.
Following are a few of the Responsibilities of a Machine Learning Engineer
- Creating Machine Learning programs using ML libraries
- Experimenting with various machine learning programs to test their efficiency
- Adapting Machine Learning programs for scalability
- Maintaining data flow between database and backend systems
- Debugging custom machine learning codes
- Optimising Machine Learning technologies in production environment
Machine Learning Job Description
We are looking for a Machine Learning Engineer to develop ML algorithms and make them production-ready. Our research focuses on human-ageing related diseases. The ideal candidate must have some background in research-oriented responsibilities to support us with relevant tools and programs. S/he will cross-function to create world-class machine learning platforms to advance our research effort. They will play a critical role in defining and executing optimisation strategies in computational biology and machine learning. Our bio-chemical formulas require candidates who can handle different types of biological data in huge volumes.
Ideal candidates should be equipped with various data analysing techniques. They should have prior experience in implementing, extending, and debugging machine learning techniques. They should be able to design and build high-leverage data infrastructure and tools.
Responsibilities of a Machine Learning Engineer
- Expertise in statistical reasoning and machine learning model development
- Excellent knowledge of the object-oriented paradigm
- Experience of Python-based programming
- Excellent knowledge of Scikit-learn
- Knowledge of Tensorflow, Python, Java, Keras, Scala
A sub-branch of Artificial Intelligence, Machine Learning demands a basic understanding of all the major AIML concepts. Machine Learning Engineers, in particular, are expected to be familiar with computer science, a little bit of data science, consumer trends and more. The following skillsets are, however, mandatory requirements to excel in the domain:
Machine Learning (ML) Skills
- Programming Language Knowledge: One of the foremost requirements of a career in Machine Learning is programming skills. There are different programming languages like Python, R, Java and C++ for different functions. While Python is the most commonly used machine learning language owing to its versatility and flexibility, other languages have their own benefits. For example, C++ is best suited to speed up your codes and R works better for statistics and plots. All these languages together help a machine language expert to understand data structures, memory management and class structure.
- Probability and Statistics: Machine Learning engineers need to be adept in statistical concepts like Mean, Regression, Gaussian Distributions and Standard Deviations. Knowledge of probability theory is important for creating algorithms for Hidden Markov models, Gaussian Mixture Models, and Naive Bayes. These probability techniques help an ML engineer to handle the uncertainties of real-world challenges. Apart from distribution models, Statistical knowledge also equips ML engineers to work on analysis methods like hypothesis testing and ANOVA. In fact, a lot of the machine learning algorithms build on existing statistical models.
- Data Modeling & Evaluation: Data modelling helps Machine Learning professionals to create or estimate the structures of any given dataset. Essentially, data modelling allows data scientists and ML engineers to prepare the data set for any specific kind of analysis. This process helps in identifying patterns (clusters, correlations, etc), predicting properties (classification, anomaly detection, regression) and creating the analysis models accordingly. The data evaluation process further helps by choosing the best model to represent the data. Data evaluating can also help in estimating the success of any data model.
- Distributed Computing: Machine Learning experts often work with large data sets which involve using multiple machines. Knowledge of projects like Apache Hadoop and cloud services like Amazon EC2 comes handy in such situations to distribute it in clusters.
- Signal Processing Techniques: Feature extraction is a crucial part of machine learning. Hence it is important for ML professionals to know signal processing techniques to solve different problems. Apart from the advanced signal processing algorithm (Wavelets, Curvelets, Bandlets, Shearlets etc), time-frequency analysis also helps ML engineers in complex problem-solving.
- Computer Science Fundamentals: Computer Science fundamentals like computer architecture, data structure, computability and complexity are important for a machine learning engineer to implement or modify programs according to requirements.
- Machine Learning Algorithms and Libraries: Even though ML libraries and packages are freely available with algorithms, not all of them are suited for all kinds of applications. ML engineers should know how to apply them effectively to optimise the outcome. Choosing the right data model, algorithm, and learning procedure is as important as knowing the libraries or languages. ML engineers should be able to discern the advantages and disadvantages of any particular algorithm and when to use them.
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A Day in the Life of a Machine Learning Engineer
Machine Learning engineers usually spend a lot of time programming but before they get into that they start their day by catching up on their emails. Pretty basic right?
You might think that ML engineers function like the rest of us, going through the day managing various routine work. However, you’d be surprised to know that Machine Learning engineers need to work on a lot of interdisciplinary tasks, ranging from data science, analytics, business communication and more. We have tried to put all the tasks together that a machine learning engineer engages in on a typical day.
- Check the models that have been active for a while
- Connect with the rest of the team for updates
- Look through task management platforms to schedule the day
- Analyse company codebase using Scikit learn to look for bugs
- Code with PyCharm to implement a model or keep the interfaces of a database running
- Meet stakeholders to ensure products are updated with new features and changes are implemented according to plans
- Discuss how to optimise products and create plans and processes for it
- Research on the latest trends in the domain and how the company can benefit from it
How to Become a Machine Learning Engineer
Machine Learning has established itself as a promising domain for professionals who want to make a difference in the fast changing digital economy. Upskilling in this field will land you lucrative offers from international brands. Great Learning’s PG program in Artificial Intelligence and Machine Learning. offers a comprehensive course structure that prepares candidates with industry insights to meet real-world challenges.