AI Engineer
Job Info
Designs, develops, and implements artificial intelligence solutions, including machine learning models, natural language processing algorithms, and other AI technologies, to address specific business challenges and enhance overall system capabilities.
Responsibilities
Design and implement machine learning algorithms, deep learning models, and other AI techniques to address specific business needs.
Collect, clean, and pre-process large datasets to ensure they are suitable for training AI models.
Train AI models using available data and evaluate their performance to ensure they meet desired accuracy and efficiency metrics.
Integrate trained models into production environments or applications, often leveraging cloud services or deploying them on edge devices.
Monitor model performance, identifying areas for improvement, and iteratively refining AI solutions to enhance their effectiveness.
Work closely with cross-functional teams, including data scientists, software engineers, and other experts, to develop comprehensive AI solutions.
Keep updated with the latest advancements in AI technologies and methodologies to incorporate cutting-edge techniques into projects.
Skills & Knowledge
Strong programming skills in languages like Python, R, or Java.
In-depth understanding of machine learning concepts, algorithms, and techniques, including supervised learning, unsupervised learning, reinforcement learning, neural networks, and deep learning architectures.
Proficiency in data manipulation and pre-processing techniques to clean, transform, and prepare datasets for model training, including experience with libraries.
Ability to design, develop, and optimise AI models and algorithms to achieve desired performance metrics, along with experience in model evaluation and hyperparameter tuning.
Familiarity with software development principles and practices, including version control systems (e.g., Git), debugging, testing, and deployment methodologies.
Knowledge of popular AI frameworks and libraries such as TensorFlow, PyTorch, Keras, and scikit-learn, and the ability to leverage them to build AI solutions.
Understanding of the specific domain or industry where AI solutions will be applied, along with knowledge of relevant business processes, data structures, and challenges.
A strong grasp of mathematical principles like linear algebra, calculus, probability, and statistics.
Strong analytical and problem-solving skills to tackle complex AI challenges, identify optimal solutions, and troubleshoot issues as they arise.
Effective communication skills to articulate technical concepts to non-technical stakeholders, collaborate with cross-functional teams, and work in interdisciplinary environments.
Career Progression
Entry-Level AI Engineer - Junior AI Engineer - Mid-Level AI Engineer - Senior AI Engineer - AI Architect or Lead - AI Researcher or Scientist - Executive or Strategic Roles such as Chief AI Officer or Vice President of AI
Throughout this progression route, continuous learning, professional development, and staying updated with the latest advancements in AI technology are essential for success in the field. Networking within the AI community and building relationships with peers and industry experts can provide valuable opportunities for growth and advancement.
Qualification Pathways
If you are looking to transfer into this role from a related role in the industry, leverage your existing experience and skills in the industry to identify transferable skills that align with a AI Engineer Role. Highlight these transferable skills on your CV and in interviews to demonstrate your suitability for positions within the sector.
If you are new to industry, follow these routes:
Step 1: Start with a bachelor's degree in a relevant field such as computer science, artificial intelligence, mathematics, statistics, or engineering.
Optional: Pursue a master's or doctoral degree in AI, machine learning, data science, or a related field to gain deeper knowledge and specialisation.
Step 2: Gain Skills and Experience by engaging in practical projects during studies to apply theoretical knowledge to real-world problems and gain practical experience. Seek work experience opportunities in AI-related roles to gain industry experience and build professional networks.
Step 3: Pursue additional training or certifications in specific areas of AI, such as natural language processing, computer vision, reinforcement learning, or deep learning. You can gain relevant certifications from organisations such as Coursera, Udacity, or Microsoft to demonstrate proficiency in AI technologies and tools.
Step 4: Start your career as an entry-level AI engineer, gaining practical experience in data preprocessing, model development, and algorithm implementation.
Step 5: Advance through various roles, gaining increasing levels of responsibility and expertise in AI technologies and applications.
Step 6: Keep updated with the latest advancements and trends in AI technology through continuous learning, online courses, workshops, and conferences.
Step 7: Contribute to open-source AI projects and collaborate with the AI community to share knowledge and stay engaged with emerging technologies.
Step 8: Build a professional network within the AI community by attending industry events, joining professional organisations, and connecting with peers, mentors, and experts in the field.
Step 9: After 10 years of service, you may be eligible to apply for Fellowship of The Institute of Telecommunications Professionals (ITP)
Other Info
Relevant Apprenticeships: