AI Engineer/Architect

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

• Advance your career with exciting professional opportunities in a fast-growing, innovative environment with a startup feel
• Innovate by transforming ideas into cutting-edge AI and Generative AI products through creative experimentation
• Share your ideas in a culture defined by entrepreneurial spirit, openness, and integrity
• Work alongside helpful, enthusiastic colleagues with strong team spirit
• Benefit from extensive training curriculum and learning programs (e.g., LinkedIn Learning)
• Contribute to holistic feedback and development processes (e.g., 360-degree feedback)
• Access opportunities to live and work abroad across international offices
• Enjoy benefits such as hybrid working, daycare allowance, corporate discounts, and wellbeing support (e.g., Headspace)
• Relax in well-equipped break areas with healthy snacks and beverages
• Connect with colleagues at frequent employee events and company gatherings

• Design scalable SaaS architectures for AI/GenAI products
• Evaluate, select, and integrate third-party libraries and open-source frameworks
• Set up databases and LLM frameworks
• Deploy and manage services securely on AWS
• Lead development of AI products for business-specific SaaS use cases
• Mentor junior team members and provide architectural oversight
• Lead development of RAG pipelines, fine-tuning workflows, and data pipelines from internal and external sources
• Define engineering standards and code quality guidelines
• Partner with MLOps teams to deploy and maintain models
• Optimize performance, latency, and cost of AI/GenAI solutions
• Translate business strategy into technical direction with leadership and product teams
• Rapidly prototype new ideas and iterate based on user feedback
• Lead technical PoCs and MVP development and evaluate build-vs-buy decisions
• Stay current with AI/GenAI developments and assess new tools and models

• Proven experience designing, developing, and operating customer-facing SaaS products at scale
• Experience owning products beyond launch, including ongoing operation and evolution
• Business-oriented and data-driven with passion for delivering tangible client value
• Excellent communication skills across technical and non-technical audiences
• Strong collaboration mindset and ability to support distributed engineering teams
• High standards for reliability, security, and long-term maintainability
• Demonstrated leadership on complex infrastructure and data-centric initiatives
• Hands-on experience building applications using GenAI and LLM technologies

• SaaS multi-tenant architectures
• Distributed systems and production API design (latency, caching, resiliency patterns)
• Event-driven architectures and data pipelines (Kafka/Kinesis)
• Deep expertise in AWS
• Strong Python skills and experience with Hugging Face Transformers, LangChain, and PyTorch
• Advanced RAG patterns (chunking, hybrid search, reranking, citations, attribution)
• Evaluation frameworks (retrieval evaluation, hallucination checks, regression testing)
• GenAI safety and guardrails (prompt injection defenses, content filtering, PII redaction)
• High-performance inference (vLLM, TensorRT-LLM), batching, quantization, and GPU cost optimization
• Multi-model routing and cost controls (fallbacks, caching, budget ceilings)
• Data modeling, data quality, schema evolution, and governance
• Vector database and embedding operations (index management, re-embedding strategies, retrieval tuning)
• CI/CD for ML, model registry, feature stores, and monitoring (drift and performance)
• Ability to define and enforce engineering standards via CI
• Threat modeling for GenAI, privacy-by-design, retention policies, and auditability