What is an AI/Machine Learning Engineer?

An Artificial Intelligence (AI)/Machine Learning (ML) Engineer is a specialized professional who plays a pivotal role in developing and implementing algorithms and predictive models that enable machines to learn and make decisions with minimal human intervention. This role This role is technical and involves a strategic understanding of how AI/ML solutions can be integrated and leveraged within an organization.

Salary Range

25th Percentile
$ 0
50th Percentile
$ 0
75th Percentile
$ 0

Foundational Skills

Creativity, Critical Thinking, Customer Focus, Dependability, Detail Orientation, Leadership, Lifelong Learning, Mathematics, Problem-Solving, Professionalism, Reading, Science & Technology, Scheduling, Teamwork & Writing

Occupational Skills

Machine Learning Foundations

Understanding the core principles and algorithms that enable machines to learn from and make predictions based on data. It includes supervised, unsupervised, and reinforcement learning techniques.

Novice Level
  • Understands the fundamental concepts of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and deep learning

Emerging Level
  • Can apply machine learning algorithms to solve simple problems

Proficient Level
  • Can independently design and implement machine learning solutions for complex problems

Software Engineering

This focuses on the application of engineering principles to the design, development, maintenance, testing, and evaluation of software and systems that make computers or anything containing software work.

Novice Level
  • Proficient in at least one programming language, such as Python or R, and has a basic understanding of software design principles and algorithms

Emerging Level
  • Can develop and maintain machine learning software applications

Proficient Level
  • Can design, develop, and deploy scalable and maintainable machine learning software systems

Mathematics and Statistics

Centered around the use of mathematical models and techniques, and statistical knowledge to analyze data and solve problems in a variety of contexts, particularly in predicting outcomes and uncovering patterns.

Novice Level
  • Possesses a strong foundation in mathematics, including linear algebra, calculus, and probability

Emerging Level
  • Can apply mathematical and statistical techniques to machine learning problems

Proficient Level
  • Can independently analyze and interpret complex mathematical and statistical data in the context of machine learning

Data Analysis and Modeling

This involves extracting insights from data. It encompasses designing models that can analyze complex data sets to inform strategic decision-making, and the use of tools and techniques for data mining, evaluation, and the presentation of data findings.

Novice Level
  • Can perform data cleaning, preparation, and exploration tasks

Emerging Level
  • Can build and train basic machine learning models using existing libraries and tools

Proficient Level
  • Can independently design, develop, and evaluate complex machine learning models

Understanding of AI/ML Concepts

This involves the knowledge and comprehension of artificial intelligence and machine learning principles. It ranges from basic understanding of key concepts and algorithms to a deep, proficient grasp of complex AI/ML techniques and the ability to innovate in this field.

Novice Level
  • Basic knowledge of key AI/ML concepts, such as types of machine learning (supervised, unsupervised). Understanding of simple algorithms like linear regression

Emerging Level
  • Solid grasp of a wider range of algorithms, including decision trees and SVMs. Beginning to understand neural networks and their applications
Proficient Level
  • Deep understanding of complex algorithms, including deep learning and reinforcement learning.
  • Ability to innovate and adapt algorithms to new problems

Programming

This focuses on the ability to use programming languages, primarily in the context of AI and ML development. It covers a range from basic proficiency in languages like Python or R for simple tasks to advanced software engineering skills necessary for building robust AI/ML systems.

Novice Level
  • Competent in Python or R for basic tasks and analytics

  • Familiarity with data manipulation using Pandas or similar libraries

Emerging Level
  • Proficient in multiple programming languages, efficient in writing optimized code, and handling larger datasets
  • Experience with ML libraries like TensorFlow or PyTorch
Proficient Level
  • Expert in software engineering practices, capable of designing and developing robust, scalable, and efficient systems integrating AI/ML components

Data Handling

This pertains to the skills required for effective data management and processing. It includes basic data preprocessing and feature engineering, advancing to complex data manipulation and expertise in handling big data technologies and real-time data streams.

Novice Level
  • Basic data preprocessing skills, such as handling missing values and basic feature engineering

Emerging Level
  • Advanced data manipulation skills, including complex feature engineering and experience with big data technologies

Proficient Level
  • Mastery in data engineering, capable of designing and implementing sophisticated data pipelines and handling real-time data streams

Model Development and Evaluation

This area involves the skills necessary for creating and assessing AI/ML models. It encompasses the ability to develop simple models and understand basic performance metrics, progressing to the expertise in building sophisticated, efficient, and ethically sound models, along with a deep understanding of model validation and interpretability.

Novice Level
  • Ability to develop and evaluate simple models. Basic understanding of model performance metrics

Emerging Level
  • Developing more sophisticated models, including tuning hyperparameters

  • Good understanding of model validation techniques and bias-variance tradeoff

Proficient Level
  • Expert in model development, capable of building highly accurate, efficient, and robust models

  • Deep understanding
    of model interpretability and ethical implications

Resources

Experience + Training

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