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- Tasks: Lead the design and governance of enterprise-scale AI/ML platforms.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Shape the future of AI with cutting-edge technologies and impactful projects.
- Qualifications: Experience in AI architecture and strong cloud knowledge required.
- Other info: Dynamic role with excellent career advancement potential.
The predicted salary is between 72000 - 108000 £ per year.
We are seeking a highly experienced AI Architect to lead the design, standardisation, and governance of enterprise-scale AI/ML and Generative AI platforms spanning data, application, and cloud infrastructure layers. This is a senior architecture position centred on building and overseeing production-grade LLM, RAG, and agentic AI ecosystems within complex enterprise environments.
Key Responsibilities
- Define and take ownership of AI reference architectures covering data ingestion, model orchestration, inference layers, and application integration.
- Architect and implement LLM-based solutions (GPT, BERT, Transformer models), including RAG pipelines and vector database integrations.
- Establish and lead LLMOps / MLOps strategy, including model lifecycle management, CI/CD for ML, model registries, and monitoring frameworks.
- Design and deliver scalable, cloud-native AI platforms across Azure, AWS, or GCP.
- Ensure Responsible AI standards, governance, security, compliance, and adherence to non-functional requirements (NFRs).
- Partner with senior stakeholders to define AI strategy and shape roadmaps from discovery through to enterprise rollout.
Required Experience
- Demonstrated experience as an AI Architect, Machine Learning Architect, or Generative AI Architect.
- Strong hands-on expertise with LLMs, RAG architectures, LangChain, LangGraph, and prompt engineering.
- Deep cloud knowledge including Azure OpenAI, AWS Bedrock/SageMaker, or GCP Vertex AI.
- Experience with Kubernetes, Docker, microservices architectures, and API integrations.
- Proficiency in Python, MLOps, LLMOps, CI/CD pipelines, and model monitoring practices.
- Proven track record of delivering large-scale enterprise AI platforms.
Desirable Skills
- Experience working with vector databases such as Pinecone or FAISS.
- Knowledge of AI governance frameworks, compliance standards, and security architecture.
- Relevant certifications in Azure AI, AWS ML, or Kubernetes.
This opportunity is well suited to a technically hands-on architect with real-world experience delivering production AI platforms, rather than purely research-focused or academic profiles. Apply now to learn more about the opportunity.
AI Architect Generative AI / LLM / Cloud (Enterprise Scale) employer: DCV Technologies Limited
Contact Detail:
DCV Technologies Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Architect Generative AI / LLM / Cloud (Enterprise Scale)
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and cloud space. Attend meetups, webinars, or conferences where you can chat with industry leaders and potential employers. Remember, sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs, RAG architectures, or any cloud-native solutions you've built. This is your chance to demonstrate your hands-on expertise and make a lasting impression.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of AI governance frameworks and compliance standards. Be ready to discuss how you’ve implemented scalable AI platforms in the past and how you can contribute to the company’s AI strategy.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Make sure your application stands out by tailoring it to highlight your experience with LLMOps, MLOps, and cloud technologies.
We think you need these skills to ace AI Architect Generative AI / LLM / Cloud (Enterprise Scale)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the specific skills and experiences mentioned in the job description. Highlight your hands-on expertise with LLMs and cloud platforms like Azure or AWS, as we want to see how you fit into our AI Architect role.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your experience aligns with our needs. Share specific examples of your work with Generative AI and LLMs to grab our attention!
Showcase Your Projects: If you've worked on relevant projects, don’t hesitate to include them! We love seeing real-world applications of your skills, especially those involving model orchestration and cloud-native solutions.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at DCV Technologies Limited
✨Know Your AI Inside Out
Make sure you’re well-versed in the latest trends and technologies in AI, especially around LLMs and Generative AI. Brush up on your knowledge of architectures like GPT and BERT, and be ready to discuss how you've implemented these in past projects.
✨Showcase Your Hands-On Experience
Prepare to share specific examples of your hands-on work with cloud platforms like Azure, AWS, or GCP. Highlight any projects where you’ve designed scalable AI solutions or led MLOps strategies, as this will demonstrate your practical expertise.
✨Understand the Business Side
Be ready to discuss how your technical skills align with business goals. Think about how you can partner with stakeholders to define AI strategies and shape roadmaps, as this is crucial for a senior role like this one.
✨Prepare for Technical Questions
Expect deep technical questions related to model lifecycle management, CI/CD for ML, and security compliance. Practise explaining complex concepts clearly and concisely, as this will show your ability to communicate effectively with both technical and non-technical audiences.