At a Glance
- Tasks: Build and manage AI infrastructure to enhance efficiency and decision-making.
- Company: Join a forward-thinking government organisation expanding its AI team.
- Benefits: Enjoy competitive pay, health perks, and opportunities for remote work.
- Other info: Collaborative environment with strong career growth potential.
- Why this job: Make a real impact with cutting-edge AI technologies and innovative projects.
- Qualifications: Experience in AI infrastructure, programming, and ethical AI practices required.
The predicted salary is between 60000 - 80000 £ per year.
Our client, a Government organisation, are expanding its Artificial Intelligence team and is currently recruiting for an AI engineer responsible for building AI development and production infrastructure, as well as developing applications and systems that help the organisation increase efficiency, reduce costs, enhance impact, and make better business decisions. The role will also support the exploration and evaluation of emerging technologies and how these can contribute to the organisational strategy.
Beyond platform ownership, you will lead the technical execution of the organisation's most advanced AI use cases, including the development of complex agentic solutions. You will architect deep integrations, establish enterprise guardrails for secure agent enablement, and design the LLMOps pipelines and orchestration frameworks required to ensure AI workloads are reliably promoted across environments to produce safe, scalable outcomes.
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. (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. (Skill level: Practitioner)
- Data Analysis and Synthesis: You can undertake data profiling and source system analysis. (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. (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. (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. (Skill level: Practitioner)
- Systems Integration: You can define the integration build, while co-ordinating build activities across systems. (Skill level: Practitioner)
- Testing: You can review requirements and specifications, and define test conditions. (Skill level: Working)
- Turning business problems into design: You can design systems that deal with problems spanning different business areas. (Skill level: Practitioner)
Experience:
- Strong background in designing and implementing AI infrastructure: Proficient in setting up and managing enterprise cloud environments (specifically within the Microsoft Azure ecosystem, including Azure AI Foundry, Azure OpenAI, and Copilot Studio).
- Essential experience includes building robust pipelines (CI/CD, LLMOps) for efficient data processing and model lifecycle management, alongside knowledge of Infrastructure as Code (e.g., Terraform).
- Proven experience deploying AI models and developing applications: This includes hands-on experience with generative AI, retrieval-augmented generation (RAG) architectures, natural language processing (NLP) techniques, and agentic workflows.
- Demonstrated ability to explore and evaluate emerging technologies: A keen interest in staying updated with the rapid advancements in AI, LLMs, and related engineering frameworks (e.g., LangChain, Semantic Kernel).
- Technical collaboration and engineering excellence: Experience acting as a senior technical contributor, championing engineering best practices, and driving complex problem-solving.
- Experience in ensuring ethical AI practices: A strong understanding of data privacy regulations and ethical considerations specific to AI development.
Please apply today if you have the relevant skills and experience.
Senior AI Infrastructure & LLMOps Engineer employer: Allen Lane
As a leading Government organisation, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to make a meaningful impact through advanced AI technologies. Our commitment to professional development ensures that you will have ample opportunities for growth, while our focus on ethical practices in AI guarantees that your work contributes positively to society. Join us in a dynamic environment where your expertise will help shape the future of public service.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI Infrastructure & LLMOps Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech community, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI infrastructure and LLMOps. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and scenarios related to AI and data engineering. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior AI Infrastructure & LLMOps Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Senior AI Infrastructure & LLMOps Engineer. Highlight your experience with AI infrastructure, cloud environments, and any relevant projects that showcase your skills in building robust pipelines and deploying AI models.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with the organisation's goals. Don’t forget to mention your experience with emerging technologies and how you can contribute to their strategy.
Showcase Your Technical Skills:Be specific about your technical skills in your application. Mention your proficiency in tools like Terraform, Azure AI Foundry, and any experience with generative AI or NLP techniques. This will help us see how you fit into the technical landscape of the role.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates regarding your application status.
How to prepare for a job interview at Allen Lane
✨Know Your AI Infrastructure Inside Out
Make sure you’re well-versed in the specifics of AI infrastructure, especially within the Microsoft Azure ecosystem. Brush up on your knowledge of Azure AI Foundry, Azure OpenAI, and Copilot Studio, as well as CI/CD and LLMOps pipelines. Being able to discuss your hands-on experience with these tools will show that you’re ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare to discuss complex problems you've tackled in previous roles, particularly those involving AI models and applications. Think about how you’ve designed systems that address business challenges across different areas. Use specific examples to illustrate your ability to turn business problems into effective designs.
✨Communicate Like a Pro
Since this role involves liaising between technical and non-technical stakeholders, practice articulating complex concepts in simple terms. Be ready to demonstrate your active listening skills and how you manage stakeholder expectations. This will highlight your ability to bridge the gap between teams effectively.
✨Stay Updated on Emerging Technologies
Research the latest trends in AI and LLMs, and be prepared to discuss how these can impact the organisation's strategy. Show your enthusiasm for exploring new technologies and your ability to assess their feasibility. This will demonstrate your commitment to innovation and continuous learning in the field.
