At a Glance
- Tasks: Lead enterprise AI strategy and design scalable, cloud-native AI solutions.
- Company: Join a diverse and inclusive tech company focused on innovative AI solutions.
- Benefits: Enjoy flexible working, competitive salary, private medical insurance, and generous leave.
- Other info: Hybrid working model with opportunities for professional growth and mentorship.
- Why this job: Make a real impact in AI while collaborating with cross-functional teams.
- Qualifications: Experience with agentic AI frameworks, cloud platforms, and strong technical skills required.
The predicted salary is between 80000 - 100000 £ per year.
We are seeking a Senior Enterprise Architect (AI-Focused) to lead our enterprise AI strategy and drive the design of scalable, cloud-native AI solutions. This role will focus on agentic AI, multi-agent systems, RAG architectures, and enterprise AI integration aligned to business goals. We will establish best practices for AI architecture, orchestration, governance, and responsible AI, while partnering with cross-functional teams to deliver secure, production-grade solutions across the organisation. We understand the need to provide a flexible working environment partnered with team collaboration and socialisation. Therefore, we operate a hybrid working model with 3 days of working from home per week. This role is based in the London office.
Key Responsibilities
- Define and lead the enterprise AI architecture strategy, aligning agentic AI capabilities with business goals
- Design and govern scalable AI architectures, including multi-agent systems, data pipelines, and model lifecycle management
- Drive adoption of agentic AI frameworks and establish best practices for orchestration, tool usage, and system integration
- Architect and oversee RAG based solutions, ensuring effective retrieval pipelines, embedding strategies, and knowledge integration
- Lead integration of AI solutions across enterprise platforms, APIs, and third-party services
- Establish standards for prompt engineering, multi-step orchestration, and agent behaviour design
- Ensure robust governance, including security, compliance, auditability, and responsible AI practices
- Collaborate with engineering, data, and business teams to deliver production grade AI systems
- Provide technical leadership across cloud native AI platforms, particularly within Azure ecosystems
- Mentor teams on AI architecture patterns, emerging technologies, and implementation strategies
Qualifications
- Agentic AI Frameworks: LangChain, LangGraph, Microsoft Agent Framework (formerly AutoGen / Semantic Kernel), CrewAI
- Databases: SQL databases (ideally PostgreSQL) for configuration & operational data; NoSQL vector databases (ideally Azure Cosmos DB) for embeddings & retrieval
- Azure Platform: Azure AI Foundry (agent building & orchestration), Azure OpenAI Service, Azure AI Services, App Service
- AI Techniques: RAG architectures (chunking strategies, embedding models, retrieval pipelines), Data agents with Power BI integration
- Python: Agent development, ML techniques, APIs
- Prompt Engineering: System prompt design, Tool/function calling patterns, Multi-step orchestration
- API Integration: Calling and orchestrating third-party APIs for agent tool use
- C#: API development
- DevOps: CI/CD pipelines (Azure DevOps or GitHub Actions), Docker, Infrastructure as Code (Terraform or Bicep)
- Frontend Integration: Angular or React for agent-facing UIs or dashboards
- MCP (Model Context Protocol): Tool integration with agents, MCP Apps (formerly MCP-UI) for interactive UI components
- Observability & Evaluation: LangSmith or similar tracing/evaluation tools
- Streaming & Real-Time Responses: SSE, WebSockets
- Testing Non-Deterministic AI Outputs: Evaluation strategies, golden datasets, regression testing
- Cost Management & Token Budgeting: Token usage, model trade-offs, caching strategies
- Multi-Agent Orchestration: Designing collaborative agent systems with delegation and handoffs
- Fine-Tuning / Model Customisation: Adapting models for domain-specific use cases
- Authentication & Identity: OAuth, managed identities
- Logging & Audit Trails: Compliance and traceability for agent decisions/actions
- Additional LLM Providers: Experience beyond Azure OpenAI (e.g., Anthropic, open-source via Ollama/vLLM)
- Graph Databases: Neo4j or similar for knowledge graph-backed agents
- Message Queues / Event-Driven Architecture: Azure Service Bus
- Security & Guardrails: Content filtering, prompt injection mitigation, PII handling
- API Design: REST / GraphQL for exposing agent capabilities
Benefits
- Employee-led groups such as Diversity, Equity and Inclusion committee, employee resource groups (Women and Allies), and Pride Event Group.
- Private Medical Insurance and Health Cash Plan.
- Competitive Personal Pension plan.
- Parental Leave Program.
- 25 days of annual leave plus Bank Holidays, and finish early 6 times a year with our "Flexi" scheme.
- Income Protection Plans.
- Tuition Reimbursement Schemes.
- Flexibility to work from anywhere in the world for two weeks out of the year.
EEO Statement
MRI is proud to be an inclusive employer. We welcome and celebrate diversity across all backgrounds, including ethnicity, religion, sexual orientation, gender identity, disability, age, military, veteran status and more. We believe that belonging is a direct result of diversity, equity, and inclusion. Those values are woven into the fabric of who we are and are foundational to our continued success. We’d love to hear from you!
Senior Enterprise Architect (AI Focused) employer: MRI Software
Contact Detail:
MRI Software Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Enterprise Architect (AI Focused)
✨Tip Number 1
Network like a pro! Connect with folks in the AI and enterprise architecture space on LinkedIn or at industry events. 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 past projects, especially those involving agentic AI and cloud-native solutions. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to AI architecture and integration. Practice explaining complex concepts in simple terms, as you'll likely need to communicate with cross-functional teams.
✨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 Enterprise Architect (AI Focused)
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI architecture and the specific technologies mentioned in the job description. We want to see how your skills align with our needs!
Showcase Your Projects: Include examples of past projects where you've designed scalable AI solutions or worked with multi-agent systems. This helps us understand your hands-on experience and how you can contribute to our team.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate clarity as much as complexity!
Apply Through Our Website: For the best chance of getting noticed, make sure to submit your application through our official website. It’s the easiest way for us to track your application and get back to you quickly!
How to prepare for a job interview at MRI Software
✨Know Your AI Frameworks
Familiarise yourself with the specific agentic AI frameworks mentioned in the job description, like LangChain and Microsoft Agent Framework. Be ready to discuss how you've used these tools in past projects and how they can be applied to the role.
✨Showcase Your Architectural Skills
Prepare to talk about your experience designing scalable AI architectures and multi-agent systems. Bring examples of past projects where you implemented RAG architectures or data pipelines, and be ready to explain your decision-making process.
✨Demonstrate Collaboration
Since this role involves working with cross-functional teams, think of examples that highlight your ability to collaborate effectively. Discuss how you've partnered with engineering, data, and business teams to deliver successful AI solutions.
✨Be Ready for Technical Questions
Brush up on your technical knowledge, especially around Azure platforms and DevOps practices. Expect questions on CI/CD pipelines, API integration, and prompt engineering. Practising coding challenges or system design scenarios can also help you feel more prepared.