AWS/ML Cloud Engineers

Hire AWS/ML Cloud Engineers

Amazon Web Services (AWS) is a vast and constantly expanding cloud computing platform. AWS provides infrastructure as a service (IaaS), platform as a service (PaaS), and packaged software as a service (SaaS). AWS services may also offer processing power, database storage, and content delivery services to a company, among other things.

Amazon Web Services (AWS) was founded in 2006 as an extension of Amazon.com’s internal infrastructure to serve its online retail operations. AWS was among the first to provide a pay-as-you-go cloud computing model, which grows up to give customers with as much processing, storage, or throughput as they need.

What does AWS/ML development entail?

AWS machine learning allows you to correctly forecast and get insights into data while reducing operational overhead to enhance user experience. AWS can help you at every stage of your ML adoption journey by providing the most comprehensive collection of artificial intelligence (AI) and machine learning (ML) services, infrastructure, and implementation tools.

Amazon Web Services (AWS) certification opens access to many of the highest-paying positions. It assists you in overcoming the risks of insecure work. You may apply for a variety of jobs if you have an AWS certification. Based on your AWS certification, the following are the top job opportunities:

  • AWS Cloud Architect: AWS Cloud Architect serves as a liaison between stakeholders and technical leadership, interacting directly with engineers and clients. Cloud architects lead implementation efforts and technical designs, ensuring that new technologies are included.
  • Cloud developers are in charge of creating business software applications and solutions. You may apply for a range of AWS roles if you have expertise with software development and a good understanding of the AWS platform. AWS certification will also help you in your profession as a cloud developer.
  • A cloud DevOps engineer is experienced in network operations and system deployment in addition to programming. As a consequence, a diverse set of skills combined with extensive knowledge and hands-on experience on the AWS platform may lead to a variety of career prospects. Furthermore, demonstrating your expertise via AWS certification almost quadruples your chances of landing an AWS job.
  • Cloud software engineer: If you’re a software developer who works in Python, Ruby, JavaScript, or C++, Amazon Web Services may help you grow your career. Your ability to design, construct, and deploy systems/software on the Amazon Web Services platform will help you acquire an AWS job. So, get an AWS certification to demonstrate your talents in software design and development and stand out in the job.

What are an AWS/ML developer’s tasks and responsibilities?

AWS developers are responsible for the following tasks:

  • Understanding the present application architecture of a company and offering comments and/or proposals to improve or alter it
  • Best practices and procedures for app deployment and infrastructure maintenance are being defined and documented.
  • Moving web applications to AWS with the assistance of an IT team or department.
  • Low-cost migration solutions are being developed, tested, and implemented.
  • Creating programs that are reusable, effective, and scalable
  • Software analysis, testing, debugging, and upgrade to guarantee that programs operate on all web browsers.
  • Building a serverless application using AWS services such as APIs, RDS instances, and Lambda.
  • Examining and evaluating programs to uncover technical flaws and provide suggestions and/or repair ideas.

How can you get started as an AWS/ML developer?

To start a career in AWS, you must have a basic grasp of standard IT-related courses. It’s critical to have a solid understanding of cloud computing as well as the confidence to learn how to utilize it correctly.

Learning AWS necessitates familiarity with hardware and software configuration, sophisticated networking skills, server setup, performance tuning abilities, operating system memory management, application deployment utility, and database or data source configuration.

It is a sound business plan to learn how to leverage AWS Machine Learning tools and services. This will assist both professionals and business divisions. This certification is particularly beneficial for experts, although it may be achieved by a new professional as well. It is advantageous for those who have experience developing, deploying, and sustaining machine learning solutions for a range of business difficulties. This exam might also be beneficial to professionals in development or data science.

To join this professional field, you must pass the AWS Certified Machine Learning Specialist Beta Exam. This service is provided by Amazon (AWS). Furthermore, participation in this program is not required to get the AWS ML Specialist credential. AWS’ credentials have become significantly more adaptable and cost-effective over time.

In addition, incorporate any relevant credentials and accomplishments in your AWS/ML cloud developer resume. This will allow recruiters to rapidly grasp your competencies and shortlist you for vacant positions.

AWS/ML development skills are necessary.

  1. Implementation

    One of the most important and in-depth talents to have as an AWS developer is the ability to deploy web applications to AWS. Not only are there several methods to deploy to AWS, but they are also constantly evolving as new techniques arise and old ones go away. As a consequence of this change, the following AWS deployment techniques should be double-checked to ensure that no new alternatives have become available. To begin, you need be experienced with manual web application deployment to Amazon Elastic Compute Cloud (EC2) servers. Once you understand it, you’ll be able to build on it and maybe construct your own automatic deployment techniques. Following that, you should be comfortable with CloudFormation and understand how to use it not only to deploy but also to build your application architecture. Elastic Beanstalk and its many services should also be familiar to you. Although views range on whether EB is the greatest or worst service for delivering applications to AWS, it is widely utilized, therefore knowledge of it is required. Finally, as the usage of containers expands, understanding how to deploy applications using Elastic Container Service (ECS) for Docker or Elastic Kubernetes Service (EKS) for Kubernetes becomes more important.
  2. Security

    AWS’s power might be a double-edged sword at times. It provides you a lot of freedom while not holding your hand. Self-sufficiency, as well as a deep grasp of the AWS Security Model and IAM, are required. The most prevalent problems and obstacles in AWS are usually driven by developers’ misunderstanding of IAM. Understanding how Roles and Policies function will help you in all aspects of your AWS job. Another difficult issue that arises on a frequent basis is the handling of secrets. AWS released a new tool called Secrets Manager last year, which simplifies the process of storing and retrieving confidential data in your online projects (such as API keys, passwords, and so on).
  3. Amazon Web Services SDK

    The AWS Software Development Kit (SDK) is the source code that allows your app to communicate with AWS. The API layer of the SDK is huge; even if you’re an expert, there are always new things you can do with it. Knowing the SDK will save you time since connecting to AWS will come naturally to you. When retrieving an item from an S3 bucket or connecting to a DynamoDB database, it’s typical for developers to be unsure where to begin. Don’t be that kind of programmer. Get some SDK experience to learn how to utilize one of the world’s most powerful technologies.
  4. Databases

    Databases are an essential component of every online business, and AWS provides several alternatives to meet that requirement. The issue is determining which database service is appropriate for your project. You risk picking the incorrect solution and stifling the development of your application if you don’t comprehend all of the possibilities and some of the advantages and downsides
  5. Troubleshooting

    If you’re a developer, you understand how annoying it may be to hit a snag. You’re probably also aware of how much simpler it is to overcome hurdles after they’ve been conquered. AWS is no exception in this sense. When you utilize AWS to solve an issue, it makes debugging and solving the next one much simpler. Unfortunately, there is no debugging roadmap. It’s just a question of going in and learning about AWS. Although IAM permissions or VPC-based access constraints (such as Security Groups) will cause the majority of your issues, there is no substitute for getting into the platform and building. You’ll encounter problems and have to find out how to fix them. Consider your experience the next time you encounter a problem and how to properly troubleshoot it.

How can I acquire a job as an AWS/ML developer?

AWS/ML engineers must work hard enough to stay up with the industry’s current breakthroughs and to consistently broaden their skills. To thrive, they must successfully and consistently adhere to the best practices in their industry. Developers should think about two things as they go forward in this respect. They may seek help from someone who is more experienced and skilled at teaching new skills while practicing. As an AWS/ML Developer, you must also hone your analytical, computer programming, and soft skills. As a consequence, designers must make certain that someone is accessible to help them.

Works provides the greatest remote AWS/ML development jobs to help you advance your career as a skilled AWS/ML developer. Working on challenging new technological and business issues may help you expand quickly. Join our global network of top developers to discover long-term, full-time remote AWS development jobs with greater pay and advancement opportunities.

Job Description

Responsibilities at work

  • Create tools for data-centric infrastructure.
  • Enhance and facilitate real-time data analytics and research
  • Next-generation ML workloads should be designed and implemented with a focus on contemporary, efficient technology.
  • Assist in the incorporation of industry best practices and the monitoring of system performance
  • Create and automate effective procedures for software and machine learning development teams.
  • Create and deploy cost-effective cloud migration plans.
  • Configure and manage cloud infrastructure components such as security and networking.
  • Create and manage AWS test instances, troubleshoot reported issues, and interact with other engineers to get a deeper understanding of the product.

Requirements

  • Bachelor’s/Master’s degree in engineering, computer science, or information technology (or equivalent experience)
  • 3+ years of professional experience in AWS, machine learning, and cloud engineering (rare exceptions for highly skilled engineers)
  • Working understanding of programming languages such as Python, Java, R, SQL, and others is required.
  • Working knowledge of machine learning, the cloud, and data pipelines
  • Knowledge of the life cycle of software/ML development, techniques, CI/CD approaches, and necessary tools
  • Experience with AWS installations and services (for example, S3, ECS/EKS, CloudWatch)
  • English fluency is required for collaboration with engineering management.
  • Work full-time (40 hours a week) with a 4-hour time difference with US time zones.

Preferred skills

  • Strong grasp of core computer science concepts such as data structures and algorithms, computability, complexity, computer architecture, and so on.
  • Working knowledge of third-party machine learning libraries such as Scikit-Learn, TensorFlow, Keras, PyTorch, and others.
  • Knowledge of contemporary build methodologies, continuous integration, unit testing, and TDD is required.
  • Ability to learn new technologies, frameworks, and algorithms rapidly and independently
  • Experience developing data pipelines and using technologies and frameworks such as Spark, Hadoop, and others.
  • Knowledge of scripting languages (Bash/Python/Groovy)
  • Knowledge of microservice software architecture, deployments, and related technologies