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What Does a Machine Learning Engineer Do? Roles, Skills, Salaries, and More

What Does a Machine Learning Engineer Do? Roles, Skills, Salaries, and More

Q: 1. What skills are required for a machine learning engineer?

Machine learning engineers need strong programming skills, knowledge of machine learning algorithms, mathematical concepts, and expertise in frameworks such as TensorFlow and PyTorch.

Q: 2. Which language is best for machine learning?

Python is considered the best language for machine learning due to its simplicity, vast libraries (like Scikit-learn), and active community support.

Q: 3. Which software is needed for machine learning?

Machine learning engineers use software like TensorFlow, Jupyter Notebooks, PyTorch, Scikit-learn, and data manipulation tools such as Pandas and NumPy.

Q: 4. Which country is best for machine learning?

The United States is one of the best countries for machine learning, with a large number of top tech companies and research institutions for machine learning professionals.

Q: 5. Which company is best for machine learning in India?

Top companies for machine learning in India include Wipro, TCS, Infosys, and AI-driven startups like Fractal Analytics and InMobi.

Q: 6. Which branch of machine learning is best?

Deep learning is considered the most popular and rapidly growing branch of machine learning, particularly for applications like natural language processing and autonomous systems.

Q: 7. Who is eligible for machine learning?

Anyone with a strong background in mathematics, computer science, or engineering can pursue machine learning.

Q: 8. Which tools are used by machine learning engineers?

Machine learning engineers use tools like Keras, Apache Spark, Scikit-learn, and cloud platforms such as AWS for training models and deploying solutions.

Q: 9. What are the four basics of machine learning?

The four basics of machine learning are data preparation, selecting the right algorithm, training the model, and evaluating its performance to ensure accurate predictions.

Q: 10. What is the responsibility of a machine learning engineer?

A machine learning engineer is responsible for designing, building, and deploying machine learning models, and also improving model accuracy.

By Kechit Goyal

Updated on Feb 25, 2025 | 10 min read | 1.3k views

Table of Contents

With the use of machine learning for automation, predictive analytics, and smarter decision-making, the demand for skilled professionals is growing. In fact, Machine Learning career opportunities will increase up to 200% in the coming decade.

So, what does a machine learning engineer do?

They build models, fine-tune parameters, and integrate these systems into applications to make predictions. They collaborate with software engineers and data scientists to ensure the machine learning models meet business objectives.

Stay ahead in data science, and artificial intelligence with our latest AI news covering real-time breakthroughs and innovations.

What Does a Machine Learning Engineer Do? Roles and Responsibilities

A machine learning engineer builds intelligent machines that can learn and improve from experience. Using concepts like deep learning and natural language processing, you can build machines that can automate tasks, such as customer response in e-commerce companies.

As industries like healthcare and finance are adopting machine learning for tasks like identifying fraud, skilled professionals are gaining demand, making it a lucrative career opportunity.

While the main duty of a machine learning engineer is to build intelligent machines, they perform functions like training the model and analyzing the performance.

Here are the core duties of a machine learning engineer.

1. Designing Machine Learning Models

Design algorithms and models suited to specific tasks, ensuring they can process data, make predictions, and improve over time.

Example: In healthcare, designing a deep learning model to detect anomalies in medical imaging data, like identifying early signs of tumors in X-rays.

2. Data Preprocessing and Feature Engineering

Feature engineering is the process of selecting the relevant input variables (features) that improve model performance.

Example: In the financial sector, cleaning up transaction records, normalizing values, and selecting relevant features to identify potential fraud patterns.

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

3. Model Training and Evaluation

Training models using labeled data and evaluating their performance using various metrics to ensure accuracy and reliability.

Example: In e-commerce, train a recommendation system model that predicts products customers are likely to purchase next and also check performance using metrics such as precision, recall, and F1 score.

Also Read: Evaluation Metrics in Machine Learning: Top 10 Metrics You Should Know

4. Model Optimization

Adjusting model parameters, applying optimization techniques, and tuning hyperparameters to achieve better results.

Example: In autonomous driving, optimizing a deep learning model responsible for object detection, ensuring it accurately identifies obstacles.

Also Read: Types of Optimizers in Deep Learning: Best Optimizers for Neural Networks in 2025

5. Model Deployment and Monitoring

Once models are trained and evaluated, they need to be deployed to production systems and continuously monitored for performance degradation.

Example: In a retail setting, you will monitor the model’s accuracy, retrain it periodically, and address any performance concerns that may arise, such as an unexpected shift in customer behavior.

6. Collaborating with Cross-Functional Teams

Machine learning engineers need to work with data scientists, software engineers, and stakeholders to align machine learning models with business objectives.

Example: In the manufacturing industry, collaborating with engineers and production managers to implement predictive maintenance models that can predict equipment failures.

7. Ensuring Data Privacy and Security

Ensure that sensitive data is securely handled in compliance with regulations like GDPR or HIPAA.

Example: While working with healthcare data, you must ensure that patient information is anonymized and stored securely to comply with privacy regulations.

Build foundational knowledge and practical skills in developing and deploying machine learning models. Enroll in upGrad’s Online Artificial Intelligence & Machine Learning Programs and grow our machine learning career.

While the responsibilities mentioned above are general for machine learning engineers, they can vary based on the engineer's experience level. Let’s explore how these responsibilities change as one gains more experience.

Key Roles and Responsibilities of a Machine Learning Engineer

Entry-level machine learning engineers mainly handle tasks like data collection and cleaning. With experience, you will perform advanced tasks like designing machine learning models and optimizing performance.

Here are the roles and responsibilities of machine learning engineers based on experience.

Experience Level	Roles and Responsibilities
Entry-level	Assist with data cleaning and preprocessing. Implement basic machine learning algorithms under supervision. Test models and report findings to senior engineers. Conduct exploratory data analysis (EDA) and visualize data.
Mid-level	Design and implement machine learning models for real-world applications. Handle data preprocessing pipelines and manage datasets. Perform feature engineering and model optimization. Collaborate with cross-functional teams to release models into production.
Senior	Lead the design and architecture of machine learning systems. Make decisions on model selection, evaluation, and optimization. Ensure scalability, performance, and model robustness in production environments. Mentor junior engineers and guide project direction.

Now that you’ve seen what a machine learning engineer does, let’s understand the skills needed for this role.

Key Skills Every Machine Learning Engineer Should Learn

As a machine learning engineer, you need both technical skills, such as data preprocessing, and workplace skills, like effective communication, to succeed in your role.

These skills enable you to build, optimize, and deploy machine learning models efficiently while collaborating effectively within teams.

Here are the key machine learning engineer skills.

Skill

Description

Technical Machine Learning Engineer Skills

Advanced Math & Programming: Strong foundation in calculus, linear algebra, and statistics, along with proficiency in programming languages like Java, Python, and R.
Example: Using Python to implement a recommendation system for an e-commerce platform, applying linear algebra to optimize model accuracy.
ML Frameworks (TensorFlow, PyTorch): Proficiency in machine learning frameworks for developing, training, and deploying models.
Example: Using TensorFlow to create a deep learning model that detects fraud in banking transactions based on patterns in historical data.
Data Preprocessing & Feature Engineering: Expertise in cleaning, transforming, and selecting the most relevant data features for model training.
Example: Preprocessing medical images and applying feature engineering to improve a model’s accuracy in diagnosing diseases.
Cloud Computing (AWS, Azure): Familiarity with cloud platforms to deploy models and scale applications.
Example: Deploying a predictive maintenance model for a manufacturing plant on AWS enables real-time monitoring and scaling as data volumes increase.
Deep Learning: Knowledge of advanced neural network architectures, including CNNs, RNNs, and GANs.
Example: Building a convolutional neural network (CNN) to recognize objects in satellite imagery for agricultural monitoring.

Workplace Machine Learning Engineer Skills

Team Collaboration: Ability to work effectively in teams, sharing knowledge and resources, and contributing to cross-functional projects.
Example: Collaboration with data scientists and software developers to deploy a fraud detection system in a bank’s mobile app.
Problem-Solving: Ability to identify concerns and develop effective solutions in high-pressure situations.
Example: When a model for customer churn prediction is underperforming, an engineer troubleshoots by adjusting hyperparameters.
Time Management: Prioritize tasks and handle time effectively to meet project deadlines and goals.
Example: Manages multiple machine learning projects at once, ensuring they are ready for deployment by the deadline.
Communication Skills: Clear communication of technical concepts to non-technical stakeholders, including presenting results and insights.
Example: Explaining the potential impact of a new predictive model to senior management in a retail company.
Analytical Thinking: Analyze complex datasets, identify patterns, and translate them into actionable insights.
Example: Analyzing sales data from a retail chain and identifying hidden patterns in purchasing behavior to improve targeted marketing efforts.

Also Read: Top 30 Machine Learning Skills for ML Engineers in 2024

Now that you’ve explored the technical and workplace machine learning engineer skills, let's take a look at how you can become one.

How to Become a Machine Learning Engineer: A Step-by-Step Roadmap

To become a machine learning engineer, you need a combination of education, technical skills, and hands-on experience in AI and data analytics. In addition, a project portfolio and internship experience can help you secure a job in this field.

Here are the steps involved in becoming a machine learning engineer.

Step 1: Earn a Degree or Credential

A bachelor's degree in software engineering, data science, and computer engineering is often required. Alternatively, you can pursue specialized machine learning certifications.

Example: upGrad’s Executive Diploma in Machine Learning and AI course can help you master machine learning.

Step 2: Improve Your Skills

Focus on increasing knowledge of programming languages such as Python (widely used for ML tasks), R, and Java. Learn to use popular machine learning frameworks like Keras, TensorFlow, and PyTorch to build and deploy models.

Mathematical concepts like linear algebra, calculus, and probability will also be beneficial for machine learning applications.

Example: Build a recommendation system using Python and TensorFlow for an e-commerce platform to refine both coding and model-building skills.

Learn basic Python programming to build a strong foundation for machine learning. Join a free course on Learn Basic Python Programming and prepare for a career in machine learning.

Step 3: Build a Portfolio

Create machine learning projects that solve real-world problems. Build projects like a sentiment analysis tool using natural language processing (NLP) for social media data. Upload your work to GitHub and show it to your potential employers.

Example: If you’re interested in healthcare, create a project that uses machine learning to predict patient outcomes based on medical data.

Step 4: Gain Experience

Look for internships or junior machine learning engineer roles that can help you contribute to real-world applications. By working on practical projects, you can understand the challenges involved in preparing data, building models, deployment, and performance evaluation.

Example: Internships in companies like IBM or Google will give you practical exposure to the tools and technologies used in large-scale machine learning applications.

Step 5: Apply for Jobs

Once you've gained enough practical experience and have a solid portfolio, start applying for machine learning engineer roles.

Research companies that are using machine learning for data-driven solutions. Prepare for technical interviews that may include coding challenges and algorithm design questions.

Example: If you’re applying for a role at an automotive company focused on autonomous driving, prepare for questions about computer vision and reinforcement learning.

Now that you know how to begin your career as a machine learning engineer, let’s focus on the job roles and career progression within this profession.

Job Growth and Career Outlook for Machine Learning Engineer

Roles like AI/ML specialist and machine learning architect are increasingly in demand. With leadership qualities, you can progress to higher positions such as ML Engineering Manager or Chief Data Scientist.

Here are the future career opportunities for machine learning engineers.

Career Progression	Average Annual Salary
Data Scientist	INR 12L
Machine Learning Specialist	INR 19.7L
Machine Learning Architect	INR 29.4L
Machine Learning Manager	INR 32.5L
Chief Data Scientist	INR 29L

Source: Glassdoor, Ambitionbox

Now that you’ve looked at how salary varies based on career opportunities for machine learning engineers, let’s understand how you can build a career in this field.

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How Can upGrad Help You Become a Machine Learning Engineer

A career in machine learning requires skills such as programming, proficiency in frameworks, and a strong understanding of mathematical concepts like linear regression. Internships and hands-on projects, particularly in building predictive models, can give you a competitive edge.

To develop these skills, upGrad’s courses can be your reliable partner. These courses help build your foundational knowledge while offering practical experience through real-world projects, ensuring you’re well-prepared for the industry.

Here are some courses offered by upGrad in machine learning.

Do you need help deciding which courses can help you in machine learning? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.

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Master of Science in Machine Learning & AI from LJMU	Executive Post Graduate Programme in Machine Learning & AI from IIITB	Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland
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Frequently Asked Questions

1. What skills are required for a machine learning engineer?

2. Which language is best for machine learning?

3. Which software is needed for machine learning?

4. Which country is best for machine learning?

5. Which company is best for machine learning in India?

6. Which branch of machine learning is best?

7. Who is eligible for machine learning?

8. Which tools are used by machine learning engineers?

9. What are the four basics of machine learning?

10. What is the responsibility of a machine learning engineer?

11. Do machine learning engineers need to know SQL and Python?

Reference Links:

https://www.glassdoor.co.in/Salaries/data-scientist-salary-SRCH_KO0,14.htm
https://www.ambitionbox.com/profile/machine-learning-specialist-salary
https://www.glassdoor.co.in/Salaries/machine-learning-architect-salary-SRCH_KO0,26.htm
https://www.glassdoor.co.in/Salaries/machine-learning-manager-salary-SRCH_KO0,24.htm
https://www.glassdoor.co.in/Salaries/chief-data-scientist-salary-SRCH_KO0,20.htm
https://www.turing.com/kb/machine-learning-career-guide-and-in-demand-ml-job-roles

Kechit Goyal

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