AI Engineer/Architect
Veröffentlicht am
- Arbeitsort
- 10115 Berlin, Berlin, Deutschland
Stellenbeschreibung
AI Engineer/Architect
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 will combine deep technical expertise with strategic vision to create AI-powered products that help transform clients’ business models and enable sustainable growth.
Within the AI Lab, we are developing cutting-edge, large-scale AI products to deliver measurable impact for our clients. You will work in an open, agile, and innovation-driven environment with strong collaboration across engineering, product, and business teams.
What Makes Us Special
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Advance your career with exciting professional opportunities in a fast-growing, innovative environment with a startup feel
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Innovate by transforming ideas into cutting-edge AI and Generative AI products through creative experimentation
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Share your ideas in a culture defined by entrepreneurial spirit, openness, and integrity
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Work alongside helpful, enthusiastic colleagues with strong team spirit
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Benefit from extensive training curriculum and learning programs (e.g., LinkedIn Learning)
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Contribute to holistic feedback and development processes (e.g., 360-degree feedback)
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Access opportunities to live and work abroad across international offices
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Enjoy benefits such as hybrid working, daycare allowance, corporate discounts, and wellbeing support (e.g., Headspace)
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Relax in well-equipped break areas with healthy snacks and beverages
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Connect with colleagues at frequent employee events and company gatherings
How You Will Create an Impact
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Design scalable SaaS architectures for AI/GenAI products
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Evaluate, select, and integrate third-party libraries and open-source frameworks
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Set up databases and LLM frameworks
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Deploy and manage services securely on AWS
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Lead development of AI products for business-specific SaaS use cases
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Mentor junior team members and provide architectural oversight
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Lead development of RAG pipelines, fine-tuning workflows, and data pipelines from internal and external sources
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Define engineering standards and code quality guidelines
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Partner with MLOps teams to deploy and maintain models
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Optimize performance, latency, and cost of AI/GenAI solutions
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Translate business strategy into technical direction with leadership and product teams
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Rapidly prototype new ideas and iterate based on user feedback
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Lead technical PoCs and MVP development and evaluate build-vs-buy decisions
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Stay current with AI/GenAI developments and assess new tools and models
About You
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Proven experience designing, developing, and operating customer-facing SaaS products at scale
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Experience owning products beyond launch, including ongoing operation and evolution
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Business-oriented and data-driven with passion for delivering tangible client value
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Excellent communication skills across technical and non-technical audiences
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Strong collaboration mindset and ability to support distributed engineering teams
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High standards for reliability, security, and long-term maintainability
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Demonstrated leadership on complex infrastructure and data-centric initiatives
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Hands-on experience building applications using GenAI and LLM technologies
Technical Skills
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SaaS multi-tenant architectures
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Distributed systems and production API design (latency, caching, resiliency patterns)
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Event-driven architectures and data pipelines (Kafka/Kinesis)
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Deep expertise in AWS
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Strong Python skills and experience with Hugging Face Transformers, LangChain, and PyTorch
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Advanced RAG patterns (chunking, hybrid search, reranking, citations, attribution)
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Evaluation frameworks (retrieval evaluation, hallucination checks, regression testing)
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GenAI safety and guardrails (prompt injection defenses, content filtering, PII redaction)
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High-performance inference (vLLM, TensorRT-LLM), batching, quantization, and GPU cost optimization
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Multi-model routing and cost controls (fallbacks, caching, budget ceilings)
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Data modeling, data quality, schema evolution, and governance
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Vector database and embedding operations (index management, re-embedding strategies, retrieval tuning)
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CI/CD for ML, model registry, feature stores, and monitoring (drift and performance)
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Ability to define and enforce engineering standards via CI
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Threat modeling for GenAI, privacy-by-design, retention policies, and auditability