AWS Machine Learning Engineer Job Description [Updated for 2025]

In this era of digital transformation, the role of AWS Machine Learning Engineers has become significantly important.

As technology continues to evolve, the need for skilled professionals who can harness, adapt, and secure our AI-based infrastructure becomes even more crucial.

But let’s delve deeper: What does an AWS Machine Learning Engineer truly do?

Whether you are:

• A job aspirant trying to understand the core of this role,
• A recruitment specialist outlining the perfect candidate,
• Or merely fascinated by the world of machine learning and AWS,

You’ve come to the right place.

Today, we present a comprehensive and customizable AWS Machine Learning Engineer job description template, crafted for easy posting on job boards or career sites.

Let’s dive in.

AWS Machine Learning Engineer Duties and Responsibilities

AWS Machine Learning Engineers apply their extensive knowledge of computer science and programming to develop and implement machine learning models on Amazon Web Services, a popular cloud platform.

Their primary duties and responsibilities include:

• Designing, developing, and deploying machine learning models using AWS technologies
• Interacting with clients to understand their business problems and developing AI-based solutions
• Conducting research and implementing new machine learning algorithms and libraries
• Using AWS services for data storage, preprocessing, and model training
• Collaborating with data engineers to build data and model pipelines
• Applying machine learning, data mining and statistical techniques to create new, scalable solutions for business problems
• Providing support for machine learning applications and infrastructure
• Maintaining up-to-date knowledge of technology standards, industry trends, emerging technologies, and software development best practices
• Ensuring compliance with data privacy regulations and best practices
• Documenting procedures for model development, deployment, and maintenance

 

AWS Machine Learning Engineer Job Description Template

Job Brief

We are seeking a skilled AWS Machine Learning Engineer to join our team.

Your main responsibility will be to leverage machine learning algorithms and AWS services to design, develop, and deploy scalable machine learning solutions.

Ideal candidates should be proficient in AWS services related to machine learning, have a strong understanding of machine learning models, and be familiar with the end-to-end machine learning development life cycle.

The role of the AWS Machine Learning Engineer will be to build efficient, high-quality machine learning solutions that comply with industry standards and best practices.

 

Responsibilities

• Design, develop, and deploy machine learning models using AWS services
• Understand business objectives and develop models that help to achieve them
• Optimize solutions for efficiency, scalability, and stability
• Implement machine learning algorithms and test their effectiveness
• Use appropriate datasets and data representation methods
• Run machine learning tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Collaborate with data engineers to build data and model pipelines
• Manage resources within the AWS cloud infrastructure
• Keep abreast of developments in the field

 

Qualifications

• Proven experience as a Machine Learning Engineer or similar role
• Understanding of data structures, data modeling, and software architecture
• Deep knowledge of math, probability, statistics, and algorithms
• Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
• Proficiency in AWS services related to machine learning (SageMaker, Redshift, S3, etc)
• Proficiency in programming languages like Python, Java, or Scala
• Familiarity with machine learning models (linear/logistic regression, random forest, XGBoost, etc.)
• BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus

 

Benefits

• 401(k)
• Health insurance
• Dental insurance
• Retirement plan
• Paid time off
• Professional development opportunities

 

Additional Information

• Job Title: AWS Machine Learning Engineer
• Work Environment: This role is fully remote, with occasional travel for team meetings or client consultations.
• Reporting Structure: Reports to the Data Science Lead or Director of Data Science.
• Salary: Salary is based upon candidate experience and qualifications, as well as market and business considerations.
• Pay Range: $140,000 minimum to $210,000 maximum
• Location: [City, State] (specify the location or indicate if remote)
• Employment Type: Full-time
• Equal Opportunity Statement: We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
• Application Instructions: Please submit your resume and a cover letter outlining your qualifications and experience to [email address or application portal].

 

What Does an AWS Machine Learning Engineer Do?

AWS Machine Learning Engineers are highly skilled professionals who utilize Amazon Web Services (AWS) technology to develop, implement, and maintain machine learning models and systems.

These experts are typically employed by tech companies, businesses in various industries that leverage big data, or as independent contractors.

Their primary responsibility is to design and build machine learning models using AWS cloud-based tools and services.

This involves data gathering, data exploration, feature selection, model training, tuning, and deployment.

These engineers also collaborate with data scientists and other stakeholders to understand their requirements and translate them into reliable machine learning algorithms.

They use AWS services like Sagemaker, Redshift, Athena, and more to accomplish these tasks.

Besides, they work on improving existing machine learning models and algorithms for performance and efficiency.

They also test these models rigorously and ensure they align with the defined objectives.

Another critical role involves maintaining and troubleshooting these machine learning systems to ensure smooth performance.

They also keep an eye on the latest industry trends and research to keep their models and systems updated.

They often work in a team setting, where communication, collaboration, and project management skills are crucial.

They may also need to present their work to non-technical team members or stakeholders, requiring a solid understanding of the business implications of their work.

 

AWS Machine Learning Engineer Qualifications and Skills

An AWS Machine Learning Engineer should have a combination of technical abilities, analytical skills and industry knowledge to build and maintain machine learning models, including:

• Strong knowledge of AWS platforms and services, especially those related to machine learning and artificial intelligence, such as SageMaker, Rekognition, and AWS Lambda.
• Experience in programming languages such as Python, Java, and R, which are commonly used in machine learning applications.
• Proficiency in machine learning algorithms and concepts, including neural networks, decision trees, and regression models.
• Ability to use data analysis tools and libraries such as NumPy, Pandas, and Scikit-learn to manipulate data and draw insights from large data sets.
• Strong analytical and problem-solving skills to identify patterns and trends in data, and to develop and optimize machine learning models based on these insights.
• Excellent communication skills to clearly explain complex machine learning concepts and models to team members and stakeholders.
• Understanding of software development methodologies and practices, including agile and devops.
• Knowledge in using Machine Learning Operations (MLOps) tools to automate the deployment, monitoring, and maintenance of machine learning models.

 

AWS Machine Learning Engineer Experience Requirements

Entry-level AWS Machine Learning Engineers often have 1 to 3 years of experience, typically gained through internships, co-op programs, or part-time roles in data science, software development, or machine learning engineering.

A solid understanding of Python or Java, as well as experience with AWS services like S3, EC2, Lambda, and Redshift, is commonly required.

Candidates with 3 to 5 years of experience usually have expanded their technical skills to include specialized knowledge of various machine learning algorithms, and have experience with libraries such as TensorFlow, Keras, PyTorch or Scikit-learn.

They may also have experience developing and deploying machine learning models using AWS SageMaker.

Those with more than 5 years of experience in the field are often proficient in building and deploying large-scale machine learning solutions, have a deep understanding of the entire ML lifecycle, and possess experience with other AWS services like Glue, Athena, or DynamoDB.

At this level, candidates may have leadership or mentoring experience and could be prepared to take on senior or managerial roles.

Regardless of the level, a strong understanding of statistics, linear algebra, calculus, and machine learning theory is typically essential for all AWS Machine Learning Engineers.

Further, a Master’s or PhD in Computer Science, Machine Learning, AI, or a related field is often highly desirable.

 

AWS Machine Learning Engineer Education and Training Requirements

An AWS Machine Learning Engineer typically requires a bachelor’s degree in Computer Science, Engineering, Mathematics or a related field.

Understanding of fundamental concepts of Machine Learning, Data Analysis, and Statistics is essential.

Courses in areas such as Artificial Intelligence (AI), Machine Learning, or Data Science can be particularly beneficial.

Many employers prefer candidates with a master’s degree or Ph.D. in these fields, especially for more complex roles.

An advanced degree often focuses on specialized areas of AI and Machine Learning, giving candidates a deep understanding of the algorithms and systems used in these fields.

Additionally, a strong background in programming languages such as Python, Java, or Scala is often required.

Knowledge of AWS cloud services and experience in implementing machine learning models using AWS SageMaker or similar platforms would be highly beneficial.

AWS provides a Machine Learning certification that demonstrates an individual’s understanding and skills in the AWS Machine Learning platform.

This certification or equivalent knowledge is frequently preferred or required by employers.

Continuing education is crucial in this role due to the rapid advancements in technology and machine learning techniques.

Many AWS Machine Learning Engineers participate in ongoing training, professional development courses, or attend relevant conferences and seminars to stay up-to-date with the latest developments.

 

AWS Machine Learning Engineer Salary Expectations

An AWS Machine Learning Engineer can expect to earn an average salary of $112,806 (USD) per year.

However, the actual salary can fluctuate depending on factors such as years of experience, specific knowledge and skills, location, and the company they work for.

 

AWS Machine Learning Engineer Job Description FAQs

What skills does an AWS Machine Learning Engineer need?

AWS Machine Learning Engineers should possess strong programming skills, preferably in Python, and knowledge of machine learning algorithms and libraries such as TensorFlow and PyTorch.

A sound understanding of AWS cloud services and their application in machine learning is crucial.

They should also have strong analytical skills, problem-solving abilities, and a deep understanding of data structures and databases.

 

Do AWS Machine Learning Engineers need a degree?

Typically, AWS Machine Learning Engineers require a bachelor’s degree in Computer Science, Data Science, Statistics, or a related field.

Many employers prefer candidates with a master’s degree or Ph.D. in these fields.

Certifications such as AWS Certified Machine Learning – Specialty can also enhance a candidate’s employability.

 

What should you look for in an AWS Machine Learning Engineer’s resume?

Look for evidence of strong programming skills, machine learning experience, and knowledge of AWS cloud services.

This can be shown through work experience, projects, or certifications.

Experience with data modeling, evaluation, and optimization, as well as familiarity with various machine learning algorithms and libraries, are also essential.

Finally, it’s beneficial if they have experience in deploying machine learning models in production environments.

 

What qualities make a good AWS Machine Learning Engineer?

A good AWS Machine Learning Engineer is curious, patient, and has an analytical mindset.

They should be eager to solve complex problems and have the ability to think critically.

Strong communication skills are also important, as they need to explain complex machine learning concepts to non-technical stakeholders.

Additionally, they should be continuously learning to stay updated with the latest developments in machine learning and cloud technologies.

 

Is it difficult to hire AWS Machine Learning Engineers?

Due to the specialized skill set required, hiring AWS Machine Learning Engineers can be challenging.

The demand for these professionals is high, and there is a limited pool of qualified candidates.

To attract top talent, companies may need to offer competitive salaries, opportunities for continued learning and development, and interesting, challenging projects.

 

Conclusion

There you have it.

Today, we’ve pulled back the veil on what it means to be an AWS Machine Learning Engineer.

Surprised?

It’s not just about developing algorithms.

It’s about sculpting the future of technology, one machine learning model at a time.

Armed with our comprehensive AWS Machine Learning Engineer job description template and practical examples, you’re ready for the next step.

But why stop there?

Go further with our job description generator. It’s your secret weapon for crafting precise job listings or honing your resume to perfection.

Remember:

Every machine learning model is a piece of a larger puzzle.

Let’s shape that future. Together.

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