AI Engineer/Architect
We are seeking an experienced AI 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.
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
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