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

How You Will Create an Impact

• 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

About You

• 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

Technical Skills

• 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