At a Glance
- Tasks: Lead AI delivery programs and design multi-agent systems for global clients.
- Company: Cutting-edge tech firm focused on AI innovation and real-world applications.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Mentorship opportunities and a collaborative environment for continuous learning.
- Why this job: Join a dynamic team and shape the future of AI technology.
- Qualifications: Experience in deploying agent-based systems and strong coding skills required.
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
Digital Implementation Architect in London employer: Response Informatics
As a Digital Implementation Architect, you will thrive in an innovative environment that champions cutting-edge AI solutions and fosters a culture of continuous learning and collaboration. Our commitment to employee growth is evident through mentorship opportunities and access to the latest technologies, ensuring you stay at the forefront of the rapidly evolving AI landscape. Located in a vibrant tech hub, we offer a dynamic work culture that values creativity and encourages impactful contributions to global client projects.
StudySmarter Expert Advice🤫
We think this is how you could land Digital Implementation 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, sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your past projects, especially those involving multi-agent systems. Use platforms like GitHub to share your code and document your thought process. This way, when you land that interview, you’ve got tangible proof of your expertise.
✨Tip Number 3
Practice makes perfect! Prepare for interviews by simulating real-world scenarios. Think about how you’d explain complex concepts to a CXO without jargon. Role-play with friends or colleagues to refine your communication skills and boost your confidence.
✨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 tailor your application to highlight your experience with AI delivery and architecture. Let’s get you that dream job!
We think you need these skills to ace Digital Implementation 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!
Be Clear and Concise:We love a good story, but keep it relevant! Use clear language to explain your experience with AI delivery programs and architecture. Avoid jargon where possible, especially when discussing technical details, so that even a CXO can understand your brilliance.
Tailor Your Application:Make sure your application speaks directly to the role of Digital Implementation Architect. Highlight your skills in agent orchestration and coding tools like LangChain or Claude Code. We want to see how your unique background fits into our needs!
Apply Through Our Website:Don't forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you're keen on joining the StudySmarter team!
How to prepare for a job interview at Response Informatics
✨Know Your Stuff
Make sure you can talk in-depth 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 you integrated them with enterprise APIs.
✨Showcase Your Problem-Solving Skills
During the interview, be ready to translate complex business problems into agent architectures. Think of examples where you've successfully run discovery workshops or solution reviews with clients. This will demonstrate your ability to engage with CXO-level stakeholders and convey technical concepts without jargon.
✨Demonstrate Your Leadership
Since this role involves mentoring junior engineers, share experiences where you've led projects or guided teams. Talk about how you've raised the quality of AI engineering within your team and contributed to internal knowledge bases. This shows that you're not just a tech whiz but also a team player.
✨Stay Current and Curious
The field of AI is constantly evolving, so show your enthusiasm for staying updated on new models and frameworks. Discuss any recent tools or techniques you've evaluated before they became mainstream. This will highlight your proactive approach and commitment to continuous learning in a fast-paced environment.
