Design and implement ML solutions, fine-tune and integrate LLMs, and collaborate on deploying intelligent features in production environments.
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
- Be an Arabic speaker
- Have at least 1 year of non-internship experience in Machine Learning.
- Strong ML and DL theory background, you don't just use things, you know how they are working under the hood.
- Experience training and fine-tuning LLMs, with practical knowledge of transformer architectures
- Solid production-level Python experience and strong software engineering fundamentals (OOP, OOD, DSA)
- Familiarity with LLM integration frameworks like HuggingFace Transformers, OpenAI, or LangChain
- Familiarity with ML data pipelines and manipulation tools (e.g., Pandas, NumPy)
- Strong research, writing, and documentation skills
- Collaborative mindset and ability to communicate technical ideas clearly
Nice-to-Have
- 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
Top Skills
AWS
Azure
Docker
GCP
Huggingface Transformers
Langchain
Numpy
Openai
Pandas
Python
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