Emerging roles

Role overview

Artificial intelligence (AI) graduates are responsible for supporting the development and implementation of AI technologies within government. They apply machine learning algorithms and data analytics to solve complex problems and enhance the capabilities of AI systems. AI graduates are crucial in developing intelligent systems that can perform tasks requiring human-like cognition and decision-making abilities.

Key responsibilities

Responsibilities in an AI role may include:

• collaborating with technology and business teams to understand the applications and implications of AI within the business
• working within interdisciplinary teams to implement AI solutions, with support, that align with project requirements
• supporting the testing, and deployment of new AI systems and enhancements to existing architectures
• monitoring AI system performance and suggesting improvement initiatives for accuracy and efficiency
• providing feedback on the ethical use of AI technologies in line with departmental standards and regulations
• stay updated with the latest AI trends, techniques, and technologies to contribute to discussions and innovation within the team
• documenting AI processes, including algorithm selection, data flow, and system architecture, ensuring clear understanding and reproducibility
• engaging in team meetings and contributing to the development of project timelines and deliverables.

Ideal candidate profile

Ideal candidates for an AI role will:

• demonstrate a customer-focused approach with a strong analytical mindset
• be self-motivated, with the ability to work independently
• possess excellent time management and communication skills
• have the ability to work collaboratively in a multidisciplinary team.

Technical skills and qualifications

• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and programming languages (e.g., Python, R, Java).
• Experience with data processing and visualisation tools (e.g., SQL, Pandas, Matplotlib).
• Familiarity with neural networks, predictive modelling, and natural language processing.
• A degree in computer science, artificial intelligence, machine learning, data science, or a related field.

Role overview

Automation developers are responsible for designing, programming, simulating, and testing automated processes and machinery. They use a variety of programming languages and development frameworks to ensure that systems operate efficiently and without error. Automation developers also enhance and maintain existing automated systems to improve performance and functionality.

Key responsibilities

Responsibilities in an automation role may include:

• collaborating with cross-functional teams to understand workflow processes and requirements
• designing and developing automated solutions, with support, using appropriate programming languages and tools
• following testing strategies to ensure the stability and efficiency of automation scripts and software
• maintaining and suggesting changes to existing automation systems to enhance their performance or to integrate new features
• monitoring the effectiveness of automation solutions and suggesting adjustments as necessary to optimise operations
• documenting automation processes and maintaining a clear record of system architectures and changes.

Ideal candidate profile

Ideal candidates for an automation role will:

• be detail-oriented and have a strong analytical mindset
• demonstrate a proactive and innovative approach to problem-solving
• be self-motivated, able to work independently, and manage multiple tasks simultaneously
• possess effective time management and interpersonal communication skills
• be adaptable and able to learn new technologies or frameworks quickly.

Technical skills and qualifications

• Proficiency in programming languages such as Python, Java, C#, or JavaScript.
• Knowledge of automation tools and frameworks (e.g., Selenium, Jenkins, Puppet, Chef).
• Familiarity with continuous integration/continuous deployment (CI/CD) methodologies.
• Knowledge of Application Programming Interface (API) integrations and developing automated data workflows.
• Your degree may be in computer science, information technology, or a related field.

Machine learning graduates are responsible for assisting with the analysis, design, and implementation of machine learning systems within government. They work closely with both the data science and engineering teams to develop algorithms that can be used to extract and interpret data. Their key responsibilities include developing data-driven models, ensuring the integrity and efficiency of machine learning applications, and staying updated with the latest industry technologies and trends.

Key responsibilities

Responsibilities in a machine learning role may include:

• collaborating with data scientists and business analysts to understand the business problems and data that could be used for machine learning
• monitoring the performance of machine learning systems and suggesting adjustments to algorithms and models
• working within cross-functional teams to prototype, refine, test, and deploy machine learning models
• researching appropriate ML algorithms and tools
• documenting model development processes, results, and learnings for knowledge sharing
• staying current with advances in machine learning techniques, frameworks, and best practices.

Ideal candidate profile

Ideal candidates for a machine learning role will:

• be analytically minded and able to interpret complex datasets
• have a strong foundation in computer science, statistics, or engineering principles
• be self-motivated, able to work independently, and capable of managing multiple projects simultaneously
• possess strong problem-solving skills with a detail-oriented mindset
• have excellent communication skills.

Technical skills and qualifications

• Experience with programming languages such as Python, R, or Java.
• Understanding of data structures, data modelling, and software architecture.
• Deep understanding of statistics and probability.
• Familiarity with cloud services (e.g., AWS, Google Cloud, Azure) used in deploying machine learning models.
• Your degree may be in computer science, mathematics, statistics, artificial intelligence, or a related field.