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