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Numrah

Machine Learning Engineer 2026

Posted Yesterday
In-Office or Remote
8 Locations
Junior
In-Office or Remote
8 Locations
Junior
The Machine Learning Engineer role involves designing, implementing, and deploying ML solutions, focusing on LLMs and collaboration with engineering teams.
The summary above was generated by AI

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 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

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 1-2 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. Production experience training and fine-tuning LLMs, with practical knowledge of transformer architectures in NLP applications.
  5. Experience with MLOps best practices.
  6. 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 APIsDocker, and cloud platforms (GCP, AWS, or Azure)
  • Interest in NLP applications, smart assistants, or chatbot systems

Top Skills

AWS
Azure
Deep Learning
Docker
GCP
Llms
Machine Learning
Mlops
Nlp
Restful Apis

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