| Job description | Contract type: Permanent
Location: London
Salary: c£65,000 per annum plus civil service pension scheme. Higher ranges may be available for exceptional candidates.
Hours: Flexible working and part time hours will be considered.
Closing date for applications is 23:59pm on Sunday 16 February.
Nationality Requirement:
• UK Nationals
• Nationals of Commonwealth countries who have the right to work in the UK
• Nationals from the EU, EEA or Switzerland with (or eligible for) status under the European Union Settlement Scheme (EUSS)
Please note, we are not able to sponsor work visas or accept temporary visas as we are looking to hire on a permanent basis. Please contact the HR Service desk (hrservicedesk@nao.org.uk) should you have any questions on your nationality eligibility.
About the National Audit Office
The National Audit Office (NAO) is the UK’s main public sector audit body. Independent of government, we have responsibility for auditing the accounts of various public sector bodies, examining the propriety of government spending, assessing risks to financial control and accountability, and reviewing the economy, efficiency and effectiveness of programmes, projects, and activities. We report directly to Parliament, through the Committee of Public Accounts of the House of Commons which uses our reports as the basis of its own investigations. We employ approx. 1,000 people, most of whom are qualified accountants, trainees, or technicians. The organisation comprises two service lines: financial audit, and value for money (VFM) audit and has a strong core of highly talented corporate teams.
The NAO welcomes applications from everyone. We value diversity in all its forms and the difference it makes to our organisation. By removing barriers and creating an inclusive culture all our people can develop and maximise their full potential. As members of the Business Disability Forum and the Disability Confident Scheme we guarantee to interview all disabled applicants who meet the minimum criteria.
The NAO supports flexible working and is happy to discuss this with you at application stage.
Context and main purpose of the job:
Introduction:
The AI engineer is a newly created role within the NAO’s Digital Services (DS) function with responsibility for building AI development and production infrastructure and developing applications and systems that can help organizations increase efficiency, cut costs, increase impact, and make better business decisions. They will support the exploration and evaluation of emerging technology, and how these can support the organisational strategy.
The postholder has responsibility for creating new applications and enriching existing applications with ML or other AI capabilities, such as NLP, image recognition or optimization. They should be able to embed, integrate and deploy AI models that best support the requirements. Tasked with providing inputs, in the form of text or images, to GenAI models to confine the set of responses the model can produce to a set that produces a desired outcome.
In this role, you will:
Optimize AI development and production environments for performance, scalability and reliability, with a focus on building effective continuous integration/continuous delivery (CI/CD) infrastructure.
Work with data scientists, data engineers and governance teams to: streamline AI delivery through AI engineering best practices; improve data integrity and pipelines for continuous operation of AI models; and secure AI pipelines in compliance with regulations and internal guidelines.
customise, optimise, re-train and maintain AI models so that other applications can interact with them.
Scan the landscape regularly for new tools that can improve AI engineering processes, and stay abreast of cutting-edge AI techniques that can deliver substantial business value.
Enable experimentation, development, evaluation and deployment of generative AI (GenAI) applications by setting up AI sandboxes for experimentation, deploy advanced prompt engineering techniques (such as retrieval-augmented generation [RAG]), fine-tune models, automate complex workflows and monitor production AI applications.
This role requires regular attendance at the NAO’s office either in Victoria, London, or at the office in Newcastle.
Responsibilities of the role:
As an AI engineer, you will be responsible for experimentation with new models, tools and frameworks, managing the environments and POCs in conjunction with developers, data scientists and business units. You will undertake development of applications/use cases for the use of AI, evaluating and deploying models to support business requirements.
In this role, your responsibilities will include:
Build AI Development and Production Infrastructure: Design and implement scalable and robust infrastructure to support the development, testing, and deployment of AI models. This includes setting up cloud environments, data pipelines, and continuous integration/continuous deployment (CI/CD) systems.
Develop AI Applications and Systems: Create innovative applications and systems that leverage AI technologies to automate processes, enhance productivity, and provide actionable insights.
Embed, Integrate, and Deploy AI Models: Ensure AI models are seamlessly embedded into existing systems and workflows. This includes model training, validation, and deployment, as well as monitoring and maintaining model performance in production environments.
Provide Inputs to Generative AI Models: Supply relevant text or image inputs to generative AI models to guide their outputs towards desired outcomes. This involves fine-tuning models and curating input data to ensure the generated content meets specific requirements.
Support Organizational Strategy with AI: Align AI initiatives with the organization’s strategic objectives. Collaborate with leadership to identify areas where AI can drive significant value and develop roadmaps for AI adoption.
Optimize AI Solutions: Continuously refine and optimize AI models and solutions to improve their accuracy, efficiency, and scalability. This includes hyperparameter tuning, model retraining, and performance benchmarking.
Collaborate with Cross-Functional Teams: Work closely with teams across the organization, including data scientists, software engineers, product managers, and business analysts, to understand their needs and integrate AI solutions that address those needs effectively.
Ensure Data and AI Ethics: Uphold ethical standards in AI development and deployment. This includes ensuring data privacy, avoiding bias in AI models, and promoting transparency and accountability in AI systems. Advocate for responsible AI practices and compliance with relevant regulations and guidelines.
Explore and Evaluate Emerging Technologies: Stay informed about the latest advancements in AI and related fields. Conduct research and pilot projects to assess the feasibility and potential impact of new technologies on the organization’s goals.
Stay Updated with AI Trends: Regularly review academic papers, attend conferences, and participate in professional networks to stay current with the latest trends and best practices in AI. Apply this knowledge to keep the organization at the cutting edge of AI technology.
Develop and maintain documentation: Create and update comprehensive documentation for all developed processes, including data sources, transformations, and storage. Ensure that documentation is clear, detailed, and accessible to all relevant stakeholders to support transparency and knowledge sharing.
Key skills / competencies required
The skill sets listed also include the corresponding skill level (awareness, working, practitioner, expert):
Applied maths, statistics and scientific practices: You can apply analytical methods including exploratory data analysis and statistical testing to a specific data set, to reach accurate and reliable conclusions. You understand and use different performance and accuracy metrics for model validation in projects, hypothesis testing and information retrieval, comparing selected applicated mathematics and statistical methods and identify their differences. You can access and use the statistical and scientific tools available within the organisation. (Skill level: Working)
Communicating between the technical and non-technical: You can listen to the needs of the technical and business stakeholders, and interpret them. You effectively manage stakeholder expectations, using active and reactive communication. You can support or host difficult discussions within the team, or with diverse senior stakeholders. (Skill level: Practitioner)
Data Analysis and Synthesis: You can undertake data profiling and source system analysis. You can present clear insights to colleagues to support the end use of the data. (Skill level: Working)
Data Innovation: You can understand the impact on the organisation of emerging trends in data tools, analysis techniques and data usage. (Skill level: working)
Data Modelling, Cleansing and Enrichment: You can build and review complex data models, ensuring adherence to standards. You can use data integration tools and languages to integrate and store data, advising teams on best practice. You can ensure data for analysis meets data quality standards and is interoperable with other data sets, enabling reuse. You can work with other data professionals to improve modelling and integration patterns and standards. (Skill level: Practitioner)
Ethics and Privacy (data science): You can show an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders. You can stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area. You identify and respond to ethical concerns in your area of responsibility. (Skill level: Practitioner)
Programming and Build (software engineering): You can use agreed standards and tools to design, code, test, correct and document moderate-to-complex programs and scripts from agreed specifications and subsequent iterations. You can collaborate with others to review specifications where appropriate. (Skill level: Practitioner)
Systems Integration: You can define the integration build, while co-ordinating build activities across systems. You understand how to undertake and support integration testing activities. (Skill level: Practitioner)
Testing: You can review requirements and specifications, and define test conditions. You can identify issues and risks associated with work, analysing and reporting test activities and results. (Skill level: Working)
Turning business problems into design: You can design systems that deal with problems spanning different business areas. You can identify links between problems to devise common solutions. You can work across multiple subject areas, producing appropriate patterns. (Skill level: Practitioner)
Experience
Proven experience in deploying AI models and developing applications: This includes hands-on experience with machine learning algorithms, natural language processing (NLP) techniques, and generative-AI models. The candidate should have a track record of successfully training, and deploying AI models in production environments.
Strong background in designing and implementing AI infrastructure: The candidate should be proficient in setting up and managing cloud environments (e.g., Azure, AWS, Google Cloud) and building data pipelines for efficient data processing and model training. Experience with continuous integration/continuous deployment (CI/CD) systems and containerization technologies (e.g., Docker, Kubernetes) is also essential.
Demonstrated ability to explore and evaluate emerging technologies: The candidate should have a keen interest in staying updated with the latest advancements in AI and related fields. They should be able to conduct research, run pilot projects, and assess the feasibility and impact of new technologies on the organisation’s strategic goals.
Experience in ensuring ethical AI practices: The candidate should have a strong understanding of data privacy regulations and ethical considerations in AI development. They should be skilled in identifying and mitigating biases in AI models, ensuring transparency and accountability, and advocating for responsible AI practices within the organisation. |
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