Back-End ML Engineers

Hire Back-end ML Engineers

As a Back-end Machine Learning Engineer, it is my responsibility to design and develop Artificial Intelligence (AI) systems that use large data sets to create algorithms capable of learning and predicting outcomes. This requires me to examine, evaluate, and organise data, as well as to conduct tests to improve the learning process. Ultimately, my goal is to be able to help create high-performance machine learning models that can be used in various applications.

If you are passionate about data, automation, and algorithms, then you should consider pursuing a career in Machine Learning. On a daily basis, you will be responsible for processing and analysing large amounts of data, designing algorithms to manage it effectively, and automating components of the system to improve its performance.

Here’s how to become a skilled back-end ML engineer.

What does Back-end ML engineering entail?

Machine Learning is a cornerstone of Artificial Intelligence (AI) research, focusing on the development of algorithms and statistical models that enable computers to autonomously complete tasks without the need for explicit instructions. As one of the most captivating and sought-after disciplines within Data Science, it is important to note that Machine Learning is not the only component of this rapidly growing field.

The utilisation of Machine Learning technology is becoming increasingly prevalent in various industries, from entertainment to healthcare. As a result, there is a growing demand for experienced Back-end Machine Learning Engineers to help companies develop and optimise their machine learning systems for a variety of applications. These applications include, but are not limited to, robots, natural language processing, and image recognition. For anyone interested in a career in Artificial Intelligence, becoming a Back-end Machine Learning Engineer is an ideal option as it offers great potential for growth and development.

What are the tasks and obligations of a Back-end ML engineer?

A Back-end ML engineer‘s tasks and responsibilities include the following:

• Back-end infrastructure, data pipelines, and machine learning models are being developed for our AI-based solutions.
• Build functioning ranking models by automating modelling procedures.
• Work with product teams and engineers to achieve your goals (especially Front-end engineers)
• Machine learning software creation, testing, deployment, maintenance, and enhancement.
• Advanced machine learning methods are evaluated, defined, and applied to text and unstructured data.
• Investigate new developments in natural language processing.
• Create and maintain the machine learning and backend codebases.
• Ensure the safety and security of your data.
• Creating and testing APIs, storage solutions, and other technical initiatives.

How to Become a Back-end Machine Learning Engineer

As a Back-end Machine Learning Engineer, it is my responsibility to develop and maintain machine learning applications and systems. This involves conducting data analysis and organisation, executing tests and experiments, and closely monitoring and optimising the learning process in order to create high-performing ML systems. To be successful in this role, I must possess a strong proficiency in Python programming, the capacity to multitask and manage several complex tasks simultaneously, and the ability to create predictive models.

In this role, you will be tasked with designing and implementing machine learning models that utilise data from online applications and other sources. Previous programming experience would be advantageous, as you will need to apply algorithms to the data collected by your models. If you have a mathematical background, have an aptitude for statistical analysis, and possess web programming skills, we encourage you to submit an application.

Let’s take a look at the abilities and methodologies you’ll need to master to be a good Back-end ML engineer:

Back-end ML engineer skills are needed

The first stage is to gain the core skills required to get a high-paying position as a Back-end ML engineer. Here’s all you need to know!

1. Algorithms for Machine Learning

   As a Machine Learning Engineer, it is essential to be well-versed with all the basic machine learning tools and understand how and where they are implemented. Generally, Machine Learning algorithms can be classified into three main categories – supervised, unsupervised and reinforcement learning methods. Some of the most commonly used ML algorithms include Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests among others. Thus, for successful completion of a ML engineering project, it is essential for students to have a thorough understanding of these methods.

2. Data modelling and analysis:

   In Machine Learning, data modelling and assessment are fundamental processes. It is essential to convert and reshape the data before using it to train the system, which is the first duty of an ML engineer. It is necessary to comprehend the structure of the data before searching for patterns that are not easily visible to the human eye. Regression, classification, clustering, dimension reduction and other machine learning algorithms all require precise and diverse datasets. Consequently, a proficient ML engineer must possess the ability to detect patterns in data and apply various modelling strategies.

3. Neural Networks

   In this era of advanced machine learning, it is essential that machine learning engineers are well-versed in the fundamentals of neural networks. Neural networks are a type of artificial intelligence that use interconnected artificial neurons to generate outputs based on specific inputs and an activation function.

4. Natural Language Processing (NLP)

   Natural Language Processing (NLP) plays a pivotal role in the advancement of Artificial Intelligence (AI). This technology enables robots to understand human language by decoding the context of words and phrases. NLP enables computers to interpret human communication by analysing texts and breaking them down into their grammar components such as phrases, keywords, and unnecessary words. The Natural Language Toolkit (NLTK) is the most popular NLP platform and provides various functions to assist computers with natural language processing.

5. Mathematical applications

   Mathematics plays a critical role in the career of a Machine Learning Engineer. It provides the necessary foundation to understand how to set parameters and determine confidence levels, as well as how to select the most appropriate machine learning approach for a given set of data. Additionally, ML engineers can take advantage of advanced statistical modelling methods related to machine learning algorithms, such as linear algebra, probability, and statistical inference, to gain greater control over datasets and tools.

Where can I get remote Back-end ML engineer jobs?

Practicing is essential for any developer who seeks to improve their skills. With consistent effort and dedication, your talents will be refined over time. It is important to have someone who can offer guidance and support when needed, and who is knowledgeable of the potential difficulties that you may encounter. Additionally, it is important to ensure that work-life balance is maintained in order to avoid burnout.

At Works, we have a selection of the best remote Back-end ML engineer jobs available that can help you achieve your career goals. Our positions provide the opportunity to work on intricate technical and commercial projects while utilising the latest technologies to increase your professional development. Not only that, but you will also be part of a global community of the most talented developers and have the chance to apply for full-time and extended remote Back-end ML engineer positions with greater pay and promotion opportunities.

Job Description

Responsibilities at work

• Constructing back-end infrastructure, data pipelines, and/or machine learning models for our AI-powered solution.
• Create usable ranking models and automate modelling procedures.
• Work with product teams and engineering personnel (especially Front-end engineers)
• Create, test, deploy, maintain, and enhance machine learning software.
• Evaluate, define, and apply cutting-edge machine learning algorithms on text and unstructured data.
• Investigate new advancements in the area of Natural Language Processing.
• Take charge of developing and maintaining the core ML and backend codebases.
• Implement data security and privacy procedures.
• Experiment with, create, and construct APIs, data storage solutions, and other technical tasks.

Requirements

• Engineering or computer science bachelor’s/degree master’s (or equivalent experience)
• At least 3 years of back-end development experience with ML/NLP is required (rare exceptions for highly skilled developers)
• Strong software development abilities, including knowledge of backend technologies such as Python, PHP, Ruby, Java, and JavaScript.
• Solid grasp of machine learning foundations and libraries such as PyTorch, TensorFlow, Numpy, Pandas, Gensim, and others.
• Experience with server-side JavaScript technologies such as Node.js, npm, webpack, babel, and others.
• Experience with microservices development tools like as Go, GRPC, SQL, and others.
• Extensive experience designing online services such as Restful, Soap, and others.
• Experience in data science and machine learning technologies such as R, Python, Tensorflow, Spark, MLflow, and others.
• Strong understanding of the Linux environment and deployment procedures.
• English fluency is required for good communication.
• Work full-time (40 hours a week) with a 4-hour overlap with US time zones.

Preferred skills

• Containerization expertise using Kubernetes and Docker.
• Expertise in developing scalable, resilient, and secure enterprise applications.
• Knowledge of cloud technologies such as AWS, GCE, and Azure.
• Understanding of Big Data technologies such as Spark, Hive, and others.
• Knowledge of Agile software development approaches.
• Self-starter with excellent time management abilities.
• Strong technical and logical reasoning skills.
• Excellent consultative and communication abilities.