Recruit Machine Learning Backend Engineers
As a Backend Machine Learning Engineer, my primary role is to design and develop Artificial Intelligence (AI) systems that use extensive datasets to generate algorithms that can learn and predict outcomes. This involves analyzing, evaluating, and organizing data, as well as conducting tests to improve the learning process. Ultimately, my objective is to help create high-performance machine learning models that can be utilized in various applications.
If you are passionate about data, automation, and algorithms, pursuing a career in Machine Learning might be for you. You will be responsible for processing and analyzing vast amounts of data, devising algorithms to manage it effectively, and automating components of the system to improve its performance on a daily basis.
Here’s how you can become a proficient Backend ML Engineer.
What is involved in Backend Machine Learning Engineering?
Machine Learning is a crucial aspect of Artificial Intelligence (AI) research focused on the creation of algorithms and statistical models that enable computers to complete tasks independently, without requiring explicit instructions. While Machine Learning is a fascinating and highly sought-after field within Data Science, it is important to note that it is not the only component of this rapidly expanding domain.
The application of Machine Learning technology is increasingly prevalent across numerous industries, ranging from entertainment to healthcare. As a result, there is a growing demand for experienced Back-end Machine Learning Engineers to assist companies in developing and optimizing their machine learning systems for various applications, such as robots, image recognition, and natural language processing. For those interested in a career in Artificial Intelligence, becoming a Back-end Machine Learning Engineer is an excellent option as it offers significant potential for growth and advancement.
What are the duties and responsibilities of a Backend Machine Learning Engineer?
Some of the responsibilities and duties of a Backend Machine Learning Engineer consist of:
- Designing back-end infrastructure, data pipelines, and machine learning models for our AI-based solutions.
- Automating modelling procedures to construct operative ranking models.
- Collaborating with product teams and engineers, particularly Front-end engineers, to achieve objectives.
- Developing, testing, deploying, maintaining, and improving machine learning software.
- Evaluating, defining, and applying advanced machine learning techniques to unstructured data and text.
- Exploring the latest advancements in natural language processing.
- Developing and maintaining the machine learning and backend codebases.
- Ensuring the safety and security of data.
- Designing and testing APIs, storage solutions, and other technical initiatives.
Becoming a Backend Machine Learning Engineer
Being a Back-end Machine Learning Engineer requires developing and maintaining machine learning applications and systems. This involves analysing and organising data, conducting tests and experiments, and closely monitoring and optimising the learning process to create high-performing ML systems. To excel in this role, mastering Python programming, multitasking and managing multiple complex tasks simultaneously, and creating predictive models are essential skills.
As a Back-end ML engineer, you will design and implement machine learning models that use data from online applications and other sources. Past programming experience is advantageous as you have to apply algorithms to the data you collect from models. If you hold a mathematical background, are proficient in statistical analysis, and possess web programming skills, we encourage you to apply.
Let’s discuss the skills and techniques you will require to become a successful Back-end ML engineer:
Skills Required for Back-end ML Engineers
To secure a lucrative position as a Back-end ML Engineer, the primary step is to acquire essential skills. Here’s everything you need to know!
Machine Learning AlgorithmsA comprehensive understanding of basic machine learning tools and their applications is essential for Machine Learning Engineers. Machine Learning algorithms can be broadly categorised into supervised, unsupervised and reinforcement learning techniques. 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, and others. Therefore, to successfully complete ML engineering projects, students need to have a thorough understanding of these techniques.
Data Modelling and Analysis:Data modelling and analysis are critical operations in Machine Learning. As an ML Engineer, the initial responsibility involves converting and restructuring data for use in system training. Understanding data structure before seeking hidden patterns is crucial. Accurate and varied datasets are crucial for regression, classification, clustering, dimensionality reduction, and other machine learning algorithms. Therefore, a skilled ML Engineer should be capable of identifying data patterns and implementing various modelling techniques.
Neural NetworksMachine Learning Engineers need to have a clear understanding of neural networks’ fundamentals in this era of advanced machine learning. Neural networks utilise interconnected artificial neurons that use input, an activation function, and interconnection weights to generate outputs.
Natural Language Processing (NLP)Natural Language Processing (NLP) plays a vital role in advancing Artificial Intelligence (AI). Robots comprehend human language using this technology by decoding the context of words and phrases. NLP helps computers interpret human communication by analysing texts and breaking them down into grammatical elements such as phrases, keywords, and unnecessary words. The Natural Language Toolkit (NLTK) is the most commonly used NLP platform, offering several functions that aid computers in natural language processing.
Mathematical ApplicationsMathematics is a critical component in a Machine Learning Engineer’s profession. It lays the groundwork for comprehending how to establish parameters, choose the most suitable approach for a given dataset, and calculate confidence levels. Additionally, ML Engineers may utilise advanced statistical modelling methods related to machine learning algorithms, including linear algebra, probability, and statistical inference, to have greater control over datasets and tools.
Where to Find Remote Back-end ML Engineer Jobs?
For developers looking to improve their skills, practice is essential. Consistent effort and dedication will refine your abilities over time. It is helpful to have someone to offer guidance and support, particularly if they are knowledgeable about the challenges you may face. Additionally, maintaining a healthy work-life balance is crucial to avoid burnout.
At Works, we offer some of the best remote Back-end ML Engineer roles to help you achieve your career objectives. Our positions provide the opportunity to work on technically and commercially challenging projects while utilising the most up-to-date technologies to advance your professional development. Not only that, but you’ll also be part of a global community of highly skilled developers and have access to full-time and extended remote Back-end ML Engineer positions with better pay and promotion opportunities.
- Developing back-end infrastructure, data pipelines, and/or machine learning models for our AI-powered solution.
- Designing usable ranking models and automating modelling procedures.
- Collaborating with product teams and engineering personnel (particularly Front-end engineers) to progress.
- Developing, testing, deploying, maintaining, and improving machine learning software.
- Assessing, defining, and utilising state-of-the-art machine learning algorithms on text and unstructured data.
- Exploring new breakthroughs in the field of Natural Language Processing.
- Assuming responsibility for building and sustaining the primary ML and backend codebases.
- Enforcing data security and privacy protocols.
- Exploring, developing, and building APIs, data storage solutions, and other technical assignments.
- Bachelor’s or master’s degree in Engineering or Computer Science (or equivalent experience)
- Minimum of 3 years of experience in back-end development with ML/NLP is mandatory (with rare exemptions for exceptionally skilled developers).
- Thorough understanding of machine learning fundamentals and frameworks like PyTorch, TensorFlow, Numpy, Pandas, Gensim, and various others.
- Expertise in microservices development tools such as Go, GRPC, SQL, and other related technologies.
- Considerable expertise in designing online services such as Restful, Soap, and other similar solutions.
- Proficiency in data science and machine learning technologies like R, Python, Tensorflow, Spark, MLflow, and other related tools.
- Thorough comprehension of Linux environment and deployment procedures.
- Proficient fluency in English language is a prerequisite for effective communication.
- Work on a full-time basis (40 hours a week) with a minimum of 4-hour overlap with the US time zones.
- Advanced proficiency in containerization using Kubernetes and Docker.
- Proficiency in developing secure, resilient, and scalable enterprise applications.
- Understanding of cloud technologies, including AWS, GCE, and Azure.
- Familiarity with Big Data technologies like Spark, Hive, and other related tools.
- Understanding of Agile software development methodologies.
- Proactive individual with exceptional time management skills.
- Robust logical reasoning and technical problem-solving abilities.
- Exceptional communication and consulting skills.