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Senior AI Solutions Engineer
Job Location:
Monthly Salary:
Not provided by the employer
Monthly Salary:
Experience Required:
5years
Experience Required:
Posted:
7 June 2026 (30+ days ago)
Posted:
Application Deadline:
4 September 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Our Client is looking to hire A Senior AI Solutions Engineer who will be design deploy integrates and support enterprise AI solutions using (Our Companys Document AI platform) plus the broader AI stack: LLMs vector databases knowledge graphs Agentic workflow frameworks and model-serving platforms. Work spans Document AI RAG over large enterprise collections knowledge-graph discovery and Agentic workflows deployed on-prem private cloud and air-gapped behind ministry firewalls.
What You Own:
- Deploy and configure Farabi-based AI solutions at client sites: OCR document understanding classification extraction summarization semantic search knowledge-base chat.
- Build and tune RAG pipelines: OCR output chunking (Arabic/English/mixed) embedding vector DB (Qdrant) re-ranking prompt design evaluation.
- Design and implement knowledge graph structures: entities relationships graph extraction GraphRAG patterns (Neo4j LightRAG).
- Build Agentic workflows in production frameworks (LangChain LangGraph n8n LlamaIndex Semantic Kernel CrewAI) with guardrails auditability and human-in-the-loop.
- Deploy and operate model-serving layer (vLLM NVIDIA NIM Triton Ollama) size GPU/inference infrastructure.
- Operate across on-prem private cloud air-gapped and public cloud environments.
- Mentor the Agentic AI Engineer. Document deployment patterns and runbooks.
Requirements
What You Bring:
- 5 years software engineering backend cloud data or solution-engineering. Real production code and deployments.
- 23 years hands-on in-production AI work (LLMs RAG agentic workflows Document AI knowledge graphs).
- At least one agentic workflow built and deployed in production with real users and failure modes handled.
- At least one RAG pipeline built and tuned in production. Diagnosed real retrieval-quality problems.
- On-prem private cloud or air-gapped AI deployment experience. Non-negotiable for Egyptian ministry clients.
- Customer-facing technical communication has explained AI decisions to non-technical stakeholders.
- ArabicEnglish bilingual at professional level (verified mid-call).
- Sees the role as client-delivery AI work not a stepping stone to AI Research or product engineering.
Nice-to-Have:
- Arabic OCR / Arabic NLP / multilingual Document AI exposure.
- Egyptian government or large-corporate AI deployment experience.
- Knowledge graph depth (Neo4j GraphRAG RDF/OWL).
- Nvidia / GPU infrastructure depth (CUDA vLLM tuning TensorRT-LLM).
- Model quantization (FP16 INT8 AWQ GPTQ GGUF).
- Fine-tuning or adapter training (LoRA QLoRA).
- LLM observability (Langfuse LangSmith OpenTelemetry).
