Machine Learning Engineer

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