AI Fullstack Developer - ONSITE - Riyadh, Saudi Arabia

Braintrust European Union

Stellenbeschreibung:

You must be located in Riyadh, Saudi Arabia or open to traveling there and staying for 6 months.

5 days a week onsite

{ALL EXPENSES WILL BE COVERED}

Role

You will be responsible for designing and developing Aiser’s end-to-end AI platform, including backend orchestration, frontend interfaces, and consensus pipelines across multiple AI providers.

About the opportunity:

We are building a next-generation AI-powered platform. The platform integrates multiple AI providers to deliver specific outputs.

Responsibilities

Architect and implement multi-provider orchestration pipelines/ workflows using LangGraph (OpenAI, Claude, DeepSeek, Mistral, Perplexity, etc.)
Develop consensus logic to merge, cross-verify, and rank model outputs.
Build scalable backend infrastructure (APIs, secure model routing, monitoring).
Deliver intuitive frontend interfaces for exporting consulting deliverables (PPT, Excel, Word, PDF, CSV).
Develop and Implement RAG pipelines (vector DBs, embeddings) for business documents and knowledge-related consulting ensuring accuracy and reduced hallucinations.
Optimize prompt engineering for structured outputs across different use cases.
Build and scale backend services to support AI-powered workflows.
Set up CI/CD pipelines, containerization (docker), and cloud deployment (AWS/Azure).
Optimize for performance, latency, and cost (premium vs. bulk AI usage).
Ensure compliance-ready architecture suitable for global rollouts (US/EU/GCC).

Requirements

Proven experience in AI full-stack development with LangGraph/LangChain (backend + frontend + AI model orchestration).
Hands-on with multiple LLM providers (at least 3 of: OpenAI, Anthropic, DeepSeek, Mistral, Perplexity).
Strong backend skills (Python, Node.js) and frontend frameworks (React/Next.js).
Expertise with vector databases (Pinecone, Weaviate, FAISS) and RAG systems.
Experience in exporting structured consulting deliverables (PPT, Excel, Word, PDF).
Strong understanding of AI cost/latency trade-offs and fallback strategies.
Excellent communication and ability to document architecture clearly.
Candidates must provide at least 1 case study (e.g., demo links, GitHub repos, architecture slides, or project briefs) demonstrating:
- Multi-provider AI orchestration or consensus systems.
- Full-stack AI platform development with consulting/business deliverables.