ML Engineers for Hire
If you want to automate prediction models, you need Machine Learning Engineers. These skilled software developers specialise in researching, designing and developing self-sustaining software applications that can learn and make forecasts using large data sets. They use artificial intelligence (AI) systems to generate and create algorithms. As well as conducting tests and refining the learning process, Machine Learning Engineers analyse, organise, and interpret data to develop high-performance machine learning models.
A career in machine learning could be a great match for those driven by data, automation and algorithms. As a machine learning professional, you would collect and analyse vast amounts of raw data, develop algorithms to process it and automate the entire process for optimal efficiency.
Machine learning offers a plethora of exciting job opportunities to those with the relevant background. You could become a Machine Learning Engineer, a Data Scientist, an NLP Scientist, a Business Intelligence Developer or a Human-Centred Machine Learning Designer, each with its own unique challenges that can further develop and refine your machine learning skills.
Exploring the Scope of Machine Learning Engineering
With many industries now requiring the skills of Machine Learning (ML) engineers, there is significant job security and opportunities for career progression. Ongoing studies have forecasted sustained growth for global Artificial Intelligence (AI) and ML sectors between 2018 and 2027. According to research conducted by International Data Corporation (IDC), the AI industry is expected to be valued at over $500 billion by 2024.
A rapidly growing demand for Artificial Intelligence (AI) and Machine Learning (ML) technology and applications has led to a significant surge in AI startups and increased interest from established businesses worldwide. Between 2010 and 2018, acquisitions of AI startups quadrupled, with investment increasing from over one billion to 8.5 billion dollars by Q1 2020. There is such a high demand for experienced ML engineers that remote job postings for ML engineers are quickly filled, evidencing the high demand from multiple industries.
Tasks and Responsibilities of an ML Engineer
As a member of the team, an ML engineer is responsible for a variety of tasks, including:
- Designing backend infrastructure, data pipelines, and/or machine learning models for an AI-powered service.
- Developing ranking models to automate and enhance modelling processes.
- Playing an active role in developing innovative features to tackle complex data management challenges.
- Deploying machine learning models to end-users and performing thorough testing.
- Building outstanding ML models by leveraging computer science fundamentals like data structures, algorithms, and machine learning.
- This course encompasses programming languages, distributed systems, and information retrieval.
- Aside from these responsibilities, additional tasks may be assigned to a Machine Learning Engineer as the ML industry is still relatively nascent and there are still many unknowns. Therefore, each company has its own approach to achieving efficient automation.
Consequently, ML engineer roles in IT companies may entail additional duties, such as:
- Collaborating with data scientists and business analysts.
- Automating infrastructure processes.
- Transforming machine learning models into APIs.
- Thoroughly testing and deploying AI/ML models.
- Minimum viable products are being generated using machine learning.
- Enterprises are utilizing AI to bring in new talent.
What’s the path to becoming a machine learning engineer?
To be effective as a Machine Learning Engineer, certain qualifications must be attained. This career involves assessing, arranging, and analysing data, performing tests and experiments, and continuously overseeing and enhancing the learning process to produce high-performing Machine Learning systems. This is a crucial position in the creation of Machine Learning applications and systems, and necessitates a high level of technical expertise.
To apply algorithms to multiple codebases as an ML Engineer, you must have a grasp of software development. A combination of mathematics, statistics, and web programming is necessary to accomplish this. These foundational skills will equip you to excel in ML Engineering positions.
Requirements to become an ML engineer
Machine Learning Engineering is a rapidly evolving and relatively new field, hence there is no specific skillset required to become an ML Engineer. Depending on your educational background, technical prowess, and areas of interest, there are numerous ways to get started. Artificial Intelligence and Machine Learning are already having a significant impact in several sectors such as Information Technology, Financial Technology, Healthcare, Education, Transportation, and many more. Companies are now transitioning from the experimentation stage, recognizing the advantages of AI, and actively pursuing the adoption of AI/ML as quickly as possible. As a result, the demand for ML Engineers is expected to continue increasing in the near future.
To excel in US job opportunities and further your career, some of the skills you must possess include:
Software engineering skillsA solid understanding of the core principles of computer science is crucial for machine learning engineers. Some examples of these principles include data structures such as multi-dimensional arrays, graphs, stacks, queues, and trees; computer architecture such as memory, clusters, deadlocks, bandwidth, and cache; and computability and complexity. Only by comprehending these fundamentals can machine learning engineers thrive in their profession.
Data science skillsAs a machine learning engineer, you must possess a firm grasp of multiple programming languages such as Python, SQL, and Java, as well as different techniques for data modelling, hypothesis testing, and evaluation. Furthermore, a strong foundation in mathematics, probability, and statistics is necessary. This includes an understanding of concepts such as conditional probability, Naive Bayes classifiers, likelihood, Bayes nets, Hidden Markov Models, and others. Additionally, it is crucial to be able to formulate an approach to assess predictive models and algorithms.
Additional machine learning skillsMany Machine Learning Engineers possess diverse skills and expertise, including deep learning, dynamic programming, neural network design, natural language processing, audio and video processing, reinforcement learning, intricate signal processing methods, and the refinement of machine learning algorithms. This expertise and knowledge allow them to create, execute, and monitor sophisticated machine learning systems with high accuracy and performance.
Security is a critical responsibility for AI/ML systemsSecurity is a vital aspect of every software solution, including Artificial Intelligence (AI) and Machine Learning (ML) systems. To guarantee that ML models are successful, it is crucial to prepare the data used to train them properly. Additionally, it is essential to strictly restrict access to the data to approved employees and applications only. Safeguarding data is a skill that must be obtained and sustained at all times.
Hands-on project experienceAs an ML engineer, it is crucial to comprehend when and how to apply your technical skills to real-world tasks and projects. Including a completed AI/ML development project in your portfolio is an excellent way to showcase your abilities and impress prospective employers, giving you an advantage when applying for remote ML engineer roles.
Communication skillsConsidering the varied stakeholders that machine learning engineers usually engage with, it is crucial for them to possess the required competencies to communicate project goals, deadlines, and anticipated results effectively to all parties involved. This includes research scientists, software engineers, data scientists and analysts, product teams, and marketing teams.
Possesses problem-solving skillsBeing able to think critically and creatively about challenges and generate solutions is a crucial skill for data scientists, software engineers, and machine learning engineers since machine learning is about providing real-time solutions to problems. Therefore, problem-solving skills are necessary for individuals in these roles.
Domain knowledgeFor Machine Learning Engineers to create self-executing software solutions and enhance solutions used by businesses and customers, they must possess a comprehensive understanding of the company’s requirements and the types of problems their designs are addressing. Without domain knowledge, a machine learning engineer’s proposals may be inaccurate, their work may overlook essential features, and the model evaluation process may be challenging.
How to discover remote ML engineer job opportunities?
As a Machine Learning Engineer, it is critical to keep up with the latest advancements in the industry to remain competitive and relevant. It is also key to practice and refine existing skills to maintain efficiency. To achieve these objectives, Machine Learning Engineers should seek out mentors who can offer guidance and advice to further enhance their analytical, artificial intelligence, machine learning, and computer programming skills. Additionally, having someone available to provide assistance when needed can be beneficial. Ultimately, these practices will help Machine Learning Engineers stay at the forefront and continue to thrive in their profession.
Works offers the most in-demand Machine Learning Engineer positions to help you achieve your career aspirations in the field of Artificial Intelligence and Machine Learning. Joining us represents an opportunity to work with cutting-edge technology and address both technical and business-related challenges, enabling you to expand your skill set and advance more rapidly in your career. Moreover, you will benefit from being part of a network of top developers and enjoying full-time, long-term remote Machine Learning Engineer employment with a superior salary and excellent prospects for career growth.
- Developing backend infrastructure, data pipelines, and/or machine learning models for our AI-driven product
- Build operational ranking models and automate modelling workflows.
- Integrate new functionality to handle complex data management challenges.
- Deploy machine learning models to end users and perform experiments.
- To develop effective machine learning models, it is crucial to have a solid understanding of computer science fundamentals such as data structures, algorithms, programming languages, distributed systems, and information retrieval. Leveraging these concepts will help ensure the highest quality and accuracy of machine learning models.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field
- Minimum of 2 years of experience in engineering and machine learning
- Thorough understanding of relevant machine learning techniques, particularly NLP, and statistics
- With a background in both data science and software engineering, I am well-equipped to deploy models and algorithms effectively in production. Besides, I possess a solid understanding of both SQL and NoSQL databases that enables me to utilise these databases effectively.
- Proficient in Python programming with excellent testing skills
- Proficiency with CI/CD (Jenkins in particular), DVC, model monitoring tools, and overall MLOps experience
- Familiarity with various machine learning techniques, including deep learning, reinforcement learning, pattern recognition, classification, and more.
- Comprehension of recommendation, targeting, ranking systems, and other similar mechanisms