Artificial Intelligence Engineer

Job Description:

An artificial intelligence engineer designs, develops, and implements AI algorithms and systems to create intelligent and automated solutions for various applications.

Job Category:
IT Industry

What you will do:

As an artificial intelligence engineer, you will be:

  • Identifying business challenges that can be addressed using AI and defining project objectives
  • Creating and optimizing AI algorithms, machine learning models, and neural networks for specific tasks
  • Cleaning, transforming, and preparing datasets to make them suitable for model training
  • Training AI models using labeled data and fine-tuning parameters for optimal performance
  • Selecting and creating relevant features from data to enhance model accuracy
  • Building software applications that incorporate AI capabilities, such as natural language processing or computer vision
  • Evaluating model accuracy, robustness, and generalization using validation techniques
  • Deploying AI models into production environments, often utilizing cloud platforms
  • Improving model performance, efficiency, and reducing inference time for real-time applications
  • Working closely with data scientists, machine learning researchers, software developers, and domain experts
  • Analysing the results of AI models, extracting insights, and making recommendations
  • Addressing ethical concerns related to bias, fairness, transparency, and privacy in AI systems
  • Documenting code, model architecture, decisions, and results for future reference and collaboration
  • Staying updated with the latest AI advancements, techniques, and research
  • Applying creative problem-solving to overcome challenges in AI model development and deployment
  • Clearly explaining technical concepts to both technical and non-technical stakeholders
  • Exploring novel AI applications and technologies to drive innovation within projects


You will need:

  • knowledge in machine learning algorithms, programming languages (like Python), data handling and analysis, mathematics and statistics, computer vision, cloud platforms, software development, optimisation, domain expertise, .
  • knowledge in ethics, problem-solving, collaboration and continuous learning
  • understanding natural language processing concepts for working with text data, sentiment analysis, language generation, and chatbots
  • familiarity with deep learning techniques, neural networks, and frameworks like TensorFlow and PyTorch

As well as:

Illustration of employee looking at workspace

Entry Requirements:

To prepare for a career as an AI engineer, consider focusing on these GCSE subjects to build a solid foundation:

  1. Mathematics: Strong math skills are essential for understanding algorithms, statistics, and mathematical concepts underlying AI.
  2. Computer Science: Learning programming languages, algorithms, and computer architecture is crucial for AI development.
  3. Physics: Physics can provide insights into optimization, algorithms, and problem-solving applicable in AI.
  4. Statistics: Understanding statistical concepts supports data analysis and model evaluation.
  5. English: Effective communication skills are valuable for collaborating and presenting findings.
  6. Design and Technology: Developing skills in design and technology can aid in creating AI-driven applications.

While these subjects provide a foundation, pursuing higher education, such as a bachelor’s degree in computer science, AI, or related fields, is typically necessary to become a proficient AI engineer.

To become an AI engineer, you generally need the following qualifications and requirements:


A bachelor’s degree in computer science, artificial intelligence, data science, machine learning, or a related field is typically required.
For more advanced roles or research positions, a master’s or PhD can be beneficial.

Mathematics and Statistics

Solid foundation in linear algebra, calculus, probability, and statistics is necessary for understanding AI algorithms.

Portfolio and Projects

Building a portfolio of AI projects to demonstrate practical skills and experience.

Certifications (Optional but advantageous)

Earning certifications from reputable platforms like Coursera, edX, or professional organizations can validate your expertise.

Practical Experience

Gaining practical experience through internships, personal projects, or entry-level AI roles.

Working Hours and Environment:

AI specialists typically work full-time hours, either in an office or remote setting, engaging in research, data analysis, model development, collaboration, and continuous learning to create AI solutions.

Career Path & Progression:

A typical career path for an AI engineer often begins with entry-level roles like junior data scientist or AI developer, advancing to mid-level positions focused on model development and optimization, and potentially progressing further to senior AI engineer, machine learning architect, AI research scientist, or AI team lead positions.