Job Description

At  Numrah , we build intelligent, modern applications that combine cutting-edge engineering with practical machine learning. We're looking for a  Machine Learning Engineer who is deeply grounded in ML theory and excited to design, train, fine-tune, and deploy  Large Language Models (LLMs)and other ML systems in real-world production environments.
You'll work closely with backend and product individuals/teams to deliver smart, scalable features—from rapid experimentation to full-scale deployment. If you're passionate about ML theory, hands-on with LLMs, and know how to ship high-impact AI features, this role is for you. What You'll Do

• Design and implement ML solutions from ideation to production
• Fine-tune and integrate LLMs
• Deploy and monitor LLM-powered features at scale in real-world products
• Collaborate with engineers and product teams to build intelligent, user-facing features
• Write clean, scalable code and detailed technical documentation
• Stay current with the latest in ML research, LLM capabilities, and MLOps best practices

Must-Haves

1. Be an Arabic speaker
2. Have at least 1 year of non-internship experience in Machine Learning.
3. Strong ML and DL theory background, you don't just use things, you know how they are working under the hood.
4. Experience  training and fine-tuning LLMs , with practical knowledge of transformer architectures
5. Solid production-level  Python experience and strong software engineering fundamentals (OOP, OOD, DSA)
6. Familiarity with  LLM integration frameworks like HuggingFace Transformers, OpenAI, or LangChain
7. Familiarity with  ML data pipelines and manipulation tools (e.g., Pandas, NumPy)
8. Strong  research, writing, and documentation skills
9. Collaborative mindset and ability to  communicate technical ideas clearly

Nice-to-Haves

• Experience deploying  LLM-based features to production
• Knowledge of  parameter-efficient fine-tuning (LoRA, QLoRA, PEFT)
• Familiarity with  RAG pipelines and  vector databases (e.g., Pinecone, Weaviate)
• Understanding of  model serving and inference optimization (quantization, batching)
• Exposure to  MLOps practices (monitoring, versioning, CI/CD for ML)
• Experience with  RESTful APIs ,  Docker , and  cloud platforms (GCP, AWS, or Azure)
• Interest in  NLP applications , smart assistants, or chatbot systems

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