Hire AWS/ML Cloud Engineers
Amazon Web Services (AWS) is an ever-growing cloud computing platform that provides businesses with Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) solutions. In addition to these services, AWS also provides businesses with access to computing power, database storage, and content delivery services.
Amazon Web Services (AWS) was established in 2006 as an offshoot of Amazon.com’s internal infrastructure to facilitate its online retail operations. AWS was one of the pioneers in offering a pay-as-you-go cloud computing model, which can be scaled up to provide customers with the necessary processing power, storage space, or data throughput they require.
What does AWS/ML development entail?
Amazon Web Services’ machine learning capabilities enable businesses to accurately anticipate and gain insights from data while reducing the operational burden to optimise the user experience. With AWS, organisations can make the most of their machine learning and artificial intelligence endeavours at every step of the journey with a comprehensive suite of services, infrastructure, and implementation tools.
Holding an Amazon Web Services (AWS) certification can open the door to a world of lucrative career opportunities. By obtaining an AWS accreditation, you are able to demonstrate your expertise in the field and have the confidence that comes with a secure job. With your AWS certification, you can apply for a wide range of positions that are in high demand and offer competitive salaries. The following are some of the top job opportunities available to those with an AWS certification:
- As an AWS Cloud Architect, you will be responsible for providing technical leadership, liaising between clients and engineers, and leading the implementation of new technologies. You will be the bridge between stakeholders and technical leadership, interfacing directly with engineers and clients to ensure that all new technologies are effectively incorporated. Furthermore, you will be responsible for designing and developing technical solutions for clients in accordance with their requirements.
- Cloud developers are responsible for developing business-oriented software applications and solutions using the Amazon Web Services (AWS) platform. To qualify for a range of roles within AWS, a strong understanding of software development and the AWS platform is required. To further advance your career in the cloud development field, obtaining AWS accreditation is highly recommended.
- A Cloud DevOps Engineer has a diverse range of skill sets, including network operations, system deployment, and programming, that are essential for success in the field. Furthermore, having hands-on experience and in-depth knowledge of the Amazon Web Services (AWS) platform can open up a variety of career prospects. Additionally, holding an AWS certification can almost quadruple the chances of securing an AWS 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?
In order to embark on a successful career in Amazon Web Services (AWS), it is essential to possess a solid foundation in several core IT topics. Acquiring a basic knowledge of cloud computing is paramount, as is the confidence to explore and use the technology correctly. With this knowledge and skill set, individuals will be well-equipped to embark on a successful career in AWS.
Acquiring a comprehensive knowledge of Amazon Web Services (AWS) requires proficiency in hardware and software configuration, networking, server setup, performance tuning, operating system memory management, application deployment, and database or data source configuration. To be successful, one must possess a comprehensive understanding of these complexities and be able to apply them to the AWS environment.
It is a sound business decision to become familiar with the AWS Machine Learning tools and services. This knowledge will be advantageous to both professionals and business divisions. This accreditation can be beneficial to experienced professionals, although it can also be beneficial to new professionals. It is particularly beneficial to those with experience in developing, deploying, and sustaining machine learning solutions to address a variety of business challenges. Additionally, it can be beneficial to professionals in development or data science.
In order to become a certified Machine Learning Specialist, individuals must pass the Amazon Web Services (AWS) Certified Machine Learning Specialist Beta Exam. Although participation in this program is not mandatory, AWS credentials have become increasingly adaptable and cost-effective in recent years.
In addition to providing a comprehensive overview of your technical skills, experience, and educational background, it is also advisable to include any relevant credentials and accomplishments in your AWS/ML cloud developer resume. This will enable recruiters to quickly and accurately identify your capabilities, allowing them to more easily include you in their shortlist of potential candidates for available positions.
AWS/ML development skills are necessary.
ImplementationHaving an in-depth understanding of effective AWS deployment techniques is essential for any AWS developer. As new techniques emerge and old ones become obsolete, it’s important to stay informed and double-check that no new alternatives have become available. To begin, manual web application deployment to Amazon Elastic Compute Cloud (EC2) servers should be well understood. Building upon this foundation, developers can begin to craft their own automatic deployment techniques. In addition, CloudFormation should be utilised to both deploy and construct the application architecture. Furthermore, Elastic Beanstalk is a widely used service that should be familiar to any AWS developer, even if there is debate over its effectiveness. Finally, as container usage increases, knowledge in deploying applications using Elastic Container Service (ECS) for Docker or Elastic Kubernetes Service (EKS) for Kubernetes is becoming more important.
SecurityThe power of Amazon Web Services (AWS) can be both a blessing and a curse. While it provides users with a great deal of flexibility, it also requires a certain level of self-sufficiency and a comprehensive understanding of the AWS Security Model and the Identity and Access Management (IAM) system. Oftentimes, the most common issues and challenges encountered in AWS can be traced back to a misunderstanding of IAM. To overcome these difficulties, it is important to learn about how Roles and Policies work. Additionally, one of the most frequent issues that arises is the management of secrets. Last year, AWS launched the Secrets Manager, a tool that simplifies the process of storing and retrieving confidential data, such as API keys and passwords, for online projects.
Amazon Web Services SDKThe AWS Software Development Kit (SDK) is an invaluable source of code that enables applications to communicate with AWS. With its extensive API layer, even experienced developers can discover new ways to utilise the SDK. By gaining proficiency in the SDK, developers can save time and confidently work with AWS services. For example, when retrieving an item from an S3 bucket or connecting to a DynamoDB database, many developers may feel unprepared and uncertain. With an understanding of the SDK, developers can leverage one of the most powerful technologies in the world.
DatabasesDatabases are a fundamental element for every web-based business, and Amazon Web Services (AWS) provides a selection of options to address this requirement. The challenge lies in deciding which database service is the best for your project. If you are not well-versed in all the available options, as well as their respective pros and cons, then you may end up choosing the wrong solution and potentially hampering the progress of your application.
TroubleshootingAs developers, we understand how frustrating and time-consuming it can be to encounter a roadblock. Fortunately, when we use AWS to solve a problem, it makes it much easier to troubleshoot and resolve the next one. Unfortunately, there is no one-size-fits-all debugging roadmap. It is simply a matter of exploring and learning the platform. While IAM permissions and VPC-based access constraints, such as Security Groups, are often the cause of most issues, the only way to resolve them is to get into the platform and start building. Through this process, you may encounter problems and have to find out how to fix them. As you gain experience, you will become better equipped to troubleshoot any issue that arises in the future.
How can I acquire a job as an AWS/ML developer?
As an AWS/ML Engineer, it is essential to stay abreast of the latest advancements in the industry and to continuously expand knowledge base. To remain successful, it is crucial to adhere to the best practices of the industry. When going forward, developers should consider two aspects. Firstly, they should seek assistance from a more experienced and capable mentor to gain new skills and practice them. Secondly, they should sharpen their analytical, programming, and soft skills as an AWS/ML Developer. Therefore, it is important to make sure that there is someone available to provide support.
At Works, we provide a wide range of remote AWS/ML development opportunities that can help you develop your skills and advance your career as an AWS/ML developer. Working on complex technological and business problems can help you grow and expand your expertise quickly. Join our global network of highly-skilled developers and find exciting, full-time remote AWS development jobs that offer more money and advancement opportunities.
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.
- 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.
- 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