Shape AI technical direction and build scalable, reliable AI products. Develop intelligent assistants, anomaly detection, and RAG pipelines. Own reliability, latency, and cost in cloud infrastructure.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
**This role requires relocation to Riyadh**
You’ll shape the technical direction and bring ambitious AI concepts to life, turning prototypes into scalable, reliable products that power next-generation customer experiences. Working closely with engineers, tech leads, and the CTO, you’ll design systems that combine cutting-edge AI, strong infrastructure, and human-centred design.
You’ll build intelligent assistants that process live conversations, extract structure from unorganised data, and surface real-time insights. You’ll develop anomaly detection and novelty discovery pipelines that blend classical time-series methods with LLM-driven context awareness.
You’ll take ownership of reliability, latency, and cost, building robust RAG pipelines, managing GPUs, and operating scalable cloud infrastructure. Collaboration is key, and you’ll integrate AI systems across products, partner with CX and product teams, and communicate impact clearly through metrics and demos.
You’ll be a great fit if you have:
- A builder’s mindset — you self-start, break silos, and own projects from prototype to production.
- 6+ years of building production ML or backend systems
- Expert Python skills and strong backend foundations (FastAPI, gRPC, Postgres, streaming/pub-sub).
- Proven experience with agent frameworks (ADK or similar) and retrieval-augmented generation (RAG) pipelines.
- Deep understanding of LLM prompting, retrieval design, evaluation, and time-series analysis (forecasting, drift, change-point detection).
- Cloud proficiency across GCP, AWS, or Azure — including networking, autoscaling, CI/CD, IaC, observability, and cost optimisation.
- Comfort with AI-native dev tools such as Cursor, v0, or Claude Code.
- Clear communication — you write concise docs, align teams, and make complex results understandable.