• Advance your career with exciting professional opportunities in our thriving company with a startup feel
• Innovate by transforming ideas into cutting-edge AI products, championing AI and Generative AI through creative experimentation to push boundaries and deliver transformative solutions.
• Voice your unique ideas in a culture defined by our entrepreneurial spirit, openness, and integrity
• Feel at home working with our helpful, enthusiastic colleagues who have great team spirit
• Broaden your perspective with our extensive training curriculum and learning programs (e.g. LinkedIn Learning)
• Speak your mind in our holistic feedback and development processes (e.g. 360-degree feedback)
• Satisfy your need for adventure with our opportunities to live and work abroad in one of our many international offices
• Enjoy our benefits, such as hybrid working, daycare allowance, corporate discounts, and wellbeing support (e.g. Headspace)
• Unwind in our break areas where you can help yourself to the healthy snacks and beverages provided
• See another side of your coworkers at our frequent employee events, World Meetings and Holiday Parties

• 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
• Work with cloud platform (AWS)to deploy and manage services securely.x
• Design and lead development of AI products for business-specific use cases that will be offered to our clients as SaaS
• Mentor and guide junior team members, setting clear technical directions and providing architectural oversight.
• Lead development of RAG and/or fine-tuning for LLMs and data pipelines for internal and external data sources (e.g., pricing databases, market benchmarks, CRM exports).
• Set engineering standards and code quality guidelines
• Work with MLOps engineers to deploy and maintain models.
• Optimize performance, latency, and cost of AI/GenAI solutions.
• Working closely with leadership, engineeringproduct team and business stakeholders, helping to translate strategy intotechnical direction.x
• Rapidly prototype new ideas and iterate based on user feedback (consultants or clients).
• Lead technical PoCs and MVP development and evaluate build-vs-buy decisions for GenAI components.
• Stay ahead of AI/GenAIdevelopments and assess new models/tools for adoption.

• Proven experience with designing, developing and operating customer-facing SaaS products used by real users at scale
• You owned SaaS products beyond initial launch, maintaining, evolving and operating them over time
• You are business-oriented data-centric, passionate about building products that deliver tangible value to our clients
• Excellent communication skills, with the ability to convey technical concepts clearly to diverse audiences.
• Very strong communication and collaboration skills - supporting other engineers, async collaboration, explaining technical decisions to non-technical audiences, writing documentation, showing initiative.
• High standards around reliability, security and long-term maintainability
• Proven leadership in managing teams through complex infrastructure and data-centric projects, encouraging collaboration and innovation.
• Proven experience indesigning and building applications using GenAI models, including open-source and commercialLLMs (code and general-purpose), including deployment/integration experience.

Technical skills

• SaaS multi-tenant architectures
• Strong requirement for distributed systems and production API design (latency, caching, backpressure, resiliency patterns).
• Experience designing event-driven architectures and data pipelines (Kafka/Kinesis) if your products need ingestion + near real-time refresh.
• Working experience with cloud platforms; deep expertise in AWS
• Strong Python skills and experience with AI frameworks like Hugging Face Transformers, LangChain, and PyTorch.
• Beyond “RAG/fine-tuning”: require experience with RAG architecture patterns (chunking, hybrid search, reranking, citations, source attribution); Evaluation frameworks (e.g., automated retrieval eval, hallucination checks, regression testing); Safety/guardrails (prompt injection defenses, content filtering, PII redaction)
• Experience with high-performance inference (vLLM/TensorRT-LLM where applicable), batching, quantization, and GPU cost/perf tradeoffs.
• Designing multi-model routing and cost controls (fallbacks, caching, budget ceilings).
• Require competence in: data modeling, data quality checks, schema evolution, and governance.
• Vector DB + embedding ops: index management, re-embedding strategies, retrieval tuning.
• CI/CD for ML, model registry, feature stores, monitoring (drift + performance).
• Ability to define engineering standards (linting, type checking, testing strategy, code review norms) and enforce via CI.
• Threat modeling for GenAI (prompt injection, data exfiltration), privacy-by-design, retention policies, and auditability.