At a Glance
- Tasks: Lead the design and delivery of cutting-edge AI systems for global clients.
- Company: Join a pioneering tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with mentorship opportunities and continuous learning.
- Why this job: Shape the future of AI with real-world applications and impactful projects.
- Qualifications: Proven experience in deploying multi-agent AI systems and strong coding skills.
The predicted salary is between 80000 - 100000 € per year.
This is not a slide-making or prompt-engineering role. We are looking for someone who has built multi-agent AI systems that run in production - not demos, not pilots that died after a sprint. You will anchor AI delivery programs end-to-end, work directly with global clients, and stay sharp on a field that changes every few weeks. You will report into and replicate the function of a senior AI delivery leader - which means you need both the depth to architect solutions and the presence to walk a CXO through what you built and why it works.
Delivery & Architecture
- Own end-to-end delivery of AI-native programs - from architecture through production deployment
- Design and build multi-agent orchestration systems using LangChain, LangGraph, CrewAI, or equivalent
- Integrate agent systems with enterprise surfaces: APIs, ERPs, CRMs, data platforms - not toy datasets
- Define agent topology: tool routing, memory strategy, state machines, fallback handling
Agentic Coding & Development
- Run agentic coding workflows using Claude Code, Cursor, OpenAI Codex, or equivalent CLI tools
- Lead projects where AI writes significant portions of the codebase - and you guide, review, and ship it
- Work with CLAUDE.md, shared context frameworks, and multi-session agent setups for team use
- Debug non-deterministic agent outputs systematically - not by gut feel
Client & Stakeholder Engagement
- Translate business problems into agent architectures for global CXO-level stakeholders
- Run discovery workshops, solution reviews, and delivery cadences with client teams
- Prepare and present technical proposals, POC plans, and roadmaps - own the story end-to-end
Team & Practice
- Mentor junior AI engineers; raise AI engineering quality across the delivery team
- Stay current: evaluate new models, frameworks, and tooling before the hype catches up
- Contribute to internal knowledge bases, reusable frameworks, and accelerators
Skills
- Agent Orchestration: LangChain, LangGraph, CrewAI - not just conceptual
- Agentic Coding Tools: Claude Code CLI, Cursor, OpenAI Codex, Copilot
- RAG & Vector Stores: Chroma, Weaviate, Pinecone - knows where RAG breaks
- LLM APIs & SDKs: Anthropic, OpenAI, Gemini - prompt design, tool use
- Python / TypeScript: Primary languages for agent + backend development
- LangSmith / Observability: Tracing, evaluation, debugging agent runs
- Cloud Platforms: Azure, AWS, GCP (at least one) - deployment, infra, managed services
- API & System Integration: REST, gRPC, Kafka - enterprise integration patterns
- MCP / Shared Context: Model Context Protocol, CLAUDE.md, Beads
- Agent Evaluation: Testing non-deterministic outputs, guardrails, evals
- CI/CD & DevOps: Git, containers, pipelines - agents need to ship
- Client Communication: Can present architecture to a CXO without jargon
What You Must Have Actually Done
- Deployed 2–3 agent-based systems in production - stateful, multi-step, real users
- Used LangGraph for multi-agent orchestration with memory, tool routing, and state management
- Built projects where AI (Claude Code, Codex, Cursor) wrote significant portions of the code
- Implemented RAG pipelines end-to-end - chunking, embedding, retrieval, re-ranking, evaluation
- Integrated agents with real enterprise APIs - not just OpenAI playground or sample data
- Debugged a production agent failure - and fixed it without blaming the model
- Can articulate when NOT to use agents - that is how we know you have built things
- Experience with Claude Code CLI in team environments (CLAUDE.md, shared context, multi-session flows)
- Familiarity with LangSmith for agent tracing, evaluation pipelines, and debugging at scale
- Has shipped something using MCP (Model Context Protocol) or similar shared-context tooling
- QA/testing mindset for agents - systematic evaluation of non-deterministic outputs
- Background in IT services or consulting - managing client expectations while building
- Experience with SLMs, fine-tuning, or on-device/edge agent deployment
AI Architect in City of London employer: Response Informatics
As an AI Architect at our company, you will thrive in a dynamic and innovative environment that champions cutting-edge technology and fosters professional growth. We offer a collaborative work culture where mentorship is encouraged, and you will have the opportunity to engage directly with global clients, shaping impactful AI solutions. With access to the latest tools and frameworks, you will be empowered to push the boundaries of AI delivery while enjoying a supportive atmosphere that values your contributions and expertise.
StudySmarter Expert Advice🤫
We think this is how you could land AI Architect in City of London
✨Tip Number 1
Network like a pro! Get out there and connect with industry folks on LinkedIn or at meetups. You never know who might have the inside scoop on an AI Architect role that’s not even advertised yet.
✨Tip Number 2
Show off your skills in real-time! Consider setting up a portfolio of your projects or even doing live coding sessions. This way, you can demonstrate your expertise in building multi-agent systems and get noticed by potential employers.
✨Tip Number 3
Don’t just apply anywhere; focus on companies that align with your values and interests. Use our website to find roles that excite you, and tailor your approach to each one. It’s all about making that personal connection!
✨Tip Number 4
Prepare for interviews by practising how to explain complex concepts simply. Remember, you’ll need to present your work to CXOs, so being able to communicate clearly is key. Mock interviews with friends can help you nail this!
We think you need these skills to ace AI Architect in City of London
Some tips for your application 🫡
Show Your Real Experience:When you're writing your application, make sure to highlight the actual projects you've shipped. We want to see the nitty-gritty of what you've built, especially if it involves multi-agent systems in production. Don't just tell us what you know; show us what you've done!
Be Clear and Concise:We love a good story, but when it comes to your application, clarity is key. Use straightforward language to explain your experience with AI architectures and coding tools. Remember, we’re looking for someone who can communicate complex ideas simply, especially to CXO-level stakeholders.
Tailor Your Application:Make sure your application speaks directly to the job description. Highlight your experience with LangChain, Claude Code, and any relevant integrations. We want to see how your skills align with what we're looking for, so don’t be shy about connecting the dots!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you get all the updates. Plus, it’s super easy – just follow the prompts and let us see what you’ve got!
How to prepare for a job interview at Response Informatics
✨Know Your Stuff
Make sure you can talk in detail about the multi-agent AI systems you've built. Be ready to discuss specific projects where you've deployed these systems in production, not just theory. Highlight your experience with tools like LangChain and Claude Code, and be prepared to explain how they fit into your architecture.
✨Speak Their Language
When you're presenting to CXO-level stakeholders, avoid jargon and keep it clear. They want to understand the value of what you've built, so focus on how your solutions solve their business problems. Practise explaining complex concepts in simple terms to ensure everyone is on the same page.
✨Showcase Your Problem-Solving Skills
Be ready to share examples of how you've debugged non-deterministic outputs or fixed production failures. This shows that you not only know how to build systems but also how to troubleshoot them effectively. Prepare a couple of stories that highlight your systematic approach to problem-solving.
✨Stay Current and Curious
The field of AI is always evolving, so demonstrate your commitment to staying updated with the latest models and frameworks. Share any recent evaluations or experiments you've conducted with new tools. This will show your passion for the field and your proactive approach to learning.
