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 while working on impactful projects with real-world applications.
- 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 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 London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI space. Attend meetups, webinars, or conferences where you can chat with industry leaders and potential employers. Remember, it’s all about who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your past projects, especially those multi-agent systems you've built. Use platforms like GitHub to share your code and document your process. This will give employers a taste of what you can do.
✨Tip Number 3
Practice your pitch! Be ready to explain your projects and the impact they had on real users. Tailor your story to highlight how you’ve tackled challenges and delivered results. Confidence is key when presenting to CXOs!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure to submit your application directly for the best chance at landing that dream role.
We think you need these skills to ace AI Architect in 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!
Tailor Your Application:Make your application stand out by tailoring it to the role. Use the language from the job description and connect your skills directly to what we're looking for. This shows us that you understand the position and are genuinely interested in joining our team.
Be Clear and Concise:We appreciate clarity! When describing your experience and skills, keep it straightforward and to the point. Avoid jargon unless it's relevant, and remember, we want to understand your journey without getting lost in technical details.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about StudySmarter and what we stand for.
How to prepare for a job interview at Response Informatics
✨Know Your Stuff
Make sure you can talk in-depth about your experience with multi-agent AI systems. Be ready to discuss specific projects where you've deployed these systems in production, not just theory. Highlight your hands-on experience with tools like LangChain and Claude Code.
✨Speak Their Language
When presenting your ideas, avoid jargon and technical terms that might confuse CXO-level stakeholders. Instead, focus on how your solutions address their business problems. Practise explaining complex concepts in simple terms to ensure clarity.
✨Showcase Your Problem-Solving Skills
Prepare to discuss real-world challenges you've faced while debugging agent failures or integrating APIs. Share specific examples of how you systematically approached these issues and what the outcomes were. This will demonstrate your practical problem-solving abilities.
✨Engage and Collaborate
Be ready to talk about your experience mentoring junior engineers and collaborating with teams. Highlight how you've contributed to knowledge sharing and improving engineering quality. This shows you're not just a lone wolf but a team player who values collaboration.
