Lead AI Software Architect/Engineer (m/f/d)
In Germany - Berlin | Bonn | Cologne | Frankfurt/Main | Hamburg | Munich
We are seeking an experienced Lead AI Architect/ Engineer to contribute to designing and building scalable SaaS products within our AI Lab. In this role, you’ll combine deep technical expertise with strategic vision to build AI-powered products that will help transform our clients’ business models and enable their growth.
Simon-Kucher is at the forefront of innovation in driving commercial excellence, revamping business models, developing solutions and methodologies for unlocking better growth of our clients. Within AI Lab, we are developing cutting-edge large scale AI products to deliver sustained top-line impact for our clients.
Are you interested in working in a team of AI evangelists with a can-do attitude? Want to experience the dynamics of agile processes in open-minded teams? How about getting creative in a startup atmosphere with a steep development curve and flat hierarchies? And most importantly, do you want to make a difference? Then you've come to the right place.
What makes us special:
1. Advance your career with exciting professional opportunities in our thriving company with a startup feel
2. Innovate by transforming ideas into cutting-edge AI products, championing AI and Generative AI through creative experimentation to push boundaries and deliver transformative solutions.
3. Voice your unique ideas in a culture defined by our entrepreneurial spirit, openness, and integrity
4. Feel at home working with our helpful, enthusiastic colleagues who have great team spirit
5. Broaden your perspective with our extensive training curriculum and learning programs (e.g. LinkedIn Learning)
6. Speak your mind in our holistic feedback and development processes (e.g. 360-degree feedback)
7. Satisfy your need for adventure with our opportunities to live and work abroad in one of our many international offices
8. Enjoy our benefits, such as hybrid working, daycare allowance, corporate discounts, and wellbeing support (e.g. Headspace)
9. Unwind in our break areas where you can help yourself to the healthy snacks and beverages provided
10. See another side of your coworkers at our frequent employee events, World Meetings and Holiday Parties
How you will create an impact:
11. Design scalable SaaS architectures for AI/GenAI products.
12. Evaluate, select, and integrate third-party libraries and open-source frameworks.
13. Set up databases and LLM frameworks
14. Work with cloud platform (AWS) to deploy and manage services securely.x
15. Design and lead development of AI products for business-specific use cases that will be offered to our clients as SaaS
16. Mentor and guide junior team members, setting clear technical directions and providing architectural oversight.
17. Lead development of RAG and/or fine-tuning for LLMs and data pipelines for internal and external data sources (e.g., pricing databases, market benchmarks, CRM exports).
18. Set engineering standards and code quality guidelines
19. Work with MLOps engineers to deploy and maintain models.
20. Optimize performance, latency, and cost of AI/GenAI solutions.
21. Working closely with leadership, engineering product team and business stakeholders, helping to translate strategy into technical direction.x
22. Rapidly prototype new ideas and iterate based on user feedback (consultants or clients).
23. Lead technical PoCs and MVP development and evaluate build-vs-buy decisions for GenAI components.
24. Stay ahead of AI/GenAI developments and assess new models/tools for adoption.
About you:
25. Proven experience with designing, developing and operating customer-facing SaaS products used by real users at scale
26. You owned SaaS products beyond initial launch, maintaining, evolving and operating them over time
27. You are business-oriented data-centric, passionate about building products that deliver tangible value to our clients
28. Excellent communication skills, with the ability to convey technical concepts clearly to diverse audiences.
29. Very strong communication and collaboration skills - supporting other engineers, async collaboration, explaining technical decisions to non-technical audiences, writing documentation, showing initiative.
30. High standards around reliability, security and long-term maintainability
31. Proven leadership in managing teams through complex infrastructure and data-centric projects, encouraging collaboration and innovation.
32. Proven experience in designing and building applications using GenAI models, including open-source and commercial LLMs (code and general-purpose), including deployment/integration experience.
Technical skills
33. SaaS multi-tenant architectures
34. Strong requirement for distributed systems and production API design (latency, caching, backpressure, resiliency patterns).
35. Experience designing event-driven architectures and data pipelines (Kafka/Kinesis) if your products need ingestion + near real-time refresh.
36. Working experience with cloud platforms; deep expertise in AWS
37. Strong Python skills and experience with AI frameworks like Hugging Face Transformers, LangChain, and PyTorch.
38. Beyond “RAG/fine-tuning”: require experience with RAG architecture patterns (chunking, hybrid search, reranking, citations, source attribution); Evaluation frameworks (e.g., automated retrieval eval, hallucination checks, regression testing); Safety/guardrails (prompt injection defenses, content filtering, PII redaction)
39. Experience with high-performance inference (vLLM/TensorRT-LLM where applicable), batching, quantization, and GPU cost/perf tradeoffs.
40. Designing multi-model routing and cost controls (fallbacks, caching, budget ceilings).
41. Require competence in: data modeling, data quality checks, schema evolution, and governance.
42. Vector DB + embedding ops: index management, re-embedding strategies, retrieval tuning.
43. CI/CD for ML, model registry, feature stores, monitoring (drift + performance).
44. Ability to define engineering standards (linting, type checking, testing strategy, code review norms) and enforce via CI.
45. Threat modeling for GenAI (prompt injection, data exfiltration), privacy-by-design, retention policies, and auditability.