Senior DevOps Engineer (AI Enablement) - Ministry of Justice - G7

Location

East Midlands (England), East of England, London (region), North East England, North West England, Scotland, South East England, South West England, Wales, West Midlands (England), Yorkshire and the Humber

The Role

Please note this role requires you to pass Security Check clearance. Please click on the link for details. 

We’re recruiting for multiple Senior DevOps Engineers here at Justice Digital, Data and Science, to be part of our warm and collaborative Hosting Platform Teams. 

This role aligns against the Senior DevOps Engineer role from the Government Digital and Data Framework. 

The Team: Justice Engineering AI Enablement is a new team focused on improving how engineers across the Ministry of Justice use artificial intelligence to design, build and operate digital services. Our aim is not simply to introduce new tools, but to establish the engineering capabilities, patterns and practices needed to use AI effectively, securely and at scale. 

The team will build production services, developer tooling and reusable capabilities that make it easier for engineers to adopt AI without compromising the quality, security, reliability or maintainability of the services they build. Our work will cover areas such as AI-assisted software development, large language models, agents, model access, evaluation, observability, security controls and developer experience. 

The work of the team has the potential to influence how hundreds of engineers across the Ministry of Justice work with AI. We will focus on creating approaches that can be reused across teams and services, rather than solving the same problems repeatedly in isolation. 

About Us: At Justice Digital, Data and Science, we use technology to improve outcomes for people across the justice system. As a Senior DevOps Engineer in Justice Engineering AI Enablement, you will help shape how emerging AI technologies are applied within one of the largest engineering communities in government. 

This is a rapidly developing area of technology, and many of the engineering patterns we will need are still emerging. We are looking for engineers who are genuinely engaged with this space and interested in helping establish how AI engineering should work in practice. 

Responsibilities: This is a hands-on engineering role. You will design, build and operate production services and tooling that enable other engineers to make effective use of AI. 

You will research and evaluate new technologies, models and engineering approaches; develop prototypes and reference implementations; and take successful ideas through to robust production capabilities. You will help establish reusable engineering patterns around areas such as AI agents, model integration, evaluation, observability, security and developer tooling. 

Collaboration: You will work closely with software developers, platform engineers, architects, security specialists, product managers and other technical professions across Justice Digital. 

You will also work with teams responsible for shared AI platforms and services, including AI gateway capabilities, helping ensure those products integrate effectively with engineering workflows and meet the needs of the wider development community. 

A significant part of the role will involve sharing knowledge, developing reusable patterns and helping the wider engineering community build the skills and confidence required to use AI effectively and responsibly. 

You can find more details of the Benefits we offer here. To help picture your life at MoJ Justice Digital, Data and Science please take a look at our blog.

Key Responsibilities

As a Senior DevOps Engineer in the Justice Engineering AI Enablement team, you will help shape how engineering teams across the Ministry of Justice use artificial intelligence to build and operate high-quality digital services.

This is a hands-on engineering role. You will design, build and operate production services, platforms and tooling that make AI capabilities secure, observable and straightforward for other engineers to adopt.

Technical:

• Design, build and operate production-grade services and tooling that enable engineers across the Ministry of Justice to make effective use of AI.
• Build developer tooling, integrations and reusable engineering patterns that reduce the friction involved in adopting AI within software delivery.
• Explore emerging AI capabilities and turn promising technologies into practical, secure and maintainable engineering solutions.
• Develop reference implementations, prototypes and proofs of concept to establish new engineering approaches, taking successful ideas through to production where appropriate.
• Help establish engineering patterns for working with large language models, including model access, tool use, agents, evaluation, observability, security and deployment.
• Work with AI platform and gateway capabilities, understanding how model gateways operate within a large enterprise and contributing to the integration of those capabilities into developer workflows.
• Design and implement approaches for safely executing AI agents and AI-generated workloads, considering areas such as filesystem access, network access, credentials, tool permissions, isolation, approvals and auditability.
• Build capabilities for evaluating and observing AI systems, enabling engineers to understand model behaviour, performance, cost and reliability.
• Help develop reusable approaches to AI security and governance, including guardrails, runtime controls and patterns that allow engineering teams to adopt AI without unnecessarily constraining innovation.
• Work across cloud platforms and AI providers, understanding the capabilities and trade-offs of different models, providers and deployment approaches.
• Stay abreast of developments in AI models, agentic systems, developer tooling and AI engineering, assessing where emerging capabilities could provide practical value to Ministry of Justice engineers.
• Work using modern software and platform engineering practices, including infrastructure as code, version control, continuous delivery and automated deployment.
• Develop and operate services across cloud platforms such as AWS, Azure and Google Cloud Platform, using appropriate programming languages and technologies for the problem being solved.
• Work with container technologies and container orchestration as part of building, deploying and operating reliable production services. 

People: 

• Work with a high degree of autonomy, identifying opportunities and engineering problems that are worth solving rather than relying solely on predefined solutions or tightly specified work.
• Collaborate with engineering teams across Justice Digital to understand where AI can improve software delivery and turn those needs into reusable capabilities rather than one-off solutions.
• Share knowledge, techniques and emerging practices with the wider engineering community, helping engineers develop the skills required to use AI effectively and responsibly.
• Help establish engineering standards and good practice for the use of AI in software development, balancing appropriate controls with the ability for teams to experiment and innovate.
• Contribute to technical discussions and decisions across the organisation, clearly communicating complex and rapidly evolving AI engineering concepts to both technical and non-technical audiences.
• Help build an open, inclusive and collaborative engineering culture in which experimentation, critical thinking and continuous learning are encouraged.
• Contribute to recruitment and the professional development of engineers within the team and the wider engineering community. 

If this feels like an exciting opportunity, something you are enthusiastic about, and want to join our team please read on and apply!

Person Specification 

You will have: 

• A systematic approach to solving complex technical problems, including the ability to work effectively where the problem, technology or preferred solution is not yet well defined.
• Experience leading technical work within a mixed-skill team and the ability to operate with significant autonomy.
• A strong practical understanding of large language models and the engineering considerations involved in integrating them into software systems.
• An understanding of model APIs, model families and providers, including the practical differences involved in consuming models through services such as Amazon Bedrock, Azure AI Foundry/Azure OpenAI and the OpenAI API.
• An understanding of AI gateways and the role they can play within an enterprise, including areas such as model routing, authentication and authorisation, policy enforcement, observability, usage management and abstraction across providers. Experience with technologies such as LiteLLM would be beneficial.
• An understanding of AI agents and tool-using systems, including emerging approaches and standards such as the Model Context Protocol (MCP).
• An understanding of the security considerations associated with AI-enabled systems, including prompt injection, guardrails, tool permissions, credentials, data access, sandboxing and isolation of agent execution.
• An understanding of approaches to observing and evaluating AI systems, including areas such as tracing, telemetry, model evaluation, quality measurement, latency and cost. Experience with platforms such as Langfuse or equivalent technologies would be beneficial.
• An understanding of developer AI tooling, such as GitHub Copilot and other AI-assisted software development tools, and the ability to communicate technical concepts, engage stakeholders and influence the adoption of these technologies across a large organisation.
• The ability and motivation to remain current with a rapidly developing technical field, critically evaluate new models, tools and techniques, and determine where they can provide genuine engineering value. 

We do not expect candidates to have previously worked with every technology or product listed above. Relevant experience may come from professional work, substantial independent learning, open-source contribution, experimentation or the development of personal projects.

What matters is that you can demonstrate a strong engineering foundation, meaningful technical engagement with modern AI systems, and the ability to turn new and emerging technology into robust solutions that other engineers can depend on.

You should be comfortable working in areas where established patterns may not yet exist, investigating unfamiliar technologies independently, challenging assumptions and helping establish the engineering practices that others will subsequently adopt. 

Willingness to be assessed against  the requirements for SC clearance.