Senior Machine Learning Engineer​/Technical Lead

The Role

We are looking for a hands‑on Senior ML Engineer to lead the development and operation of production‑grade AI systems across LLMs, OCR, and voice. This role combines deep technical ownership with leadership of a high‑performing team of junior engineers, as well as direct engagement with stakeholders to shape AI solutions.

• Lead and mentor a team of highly talented junior ML engineers.
• Conduct code reviews, design reviews, and provide technical direction.
• Enforce strong software engineering and ML best practices.
• Design, deploy, and operate scalable AI systems with a focus on reliability and performance.
• Lead production deployment of LLMs and multimodal systems (RAG, OCR, voice).
• Own model performance end-to-end
  , combining evaluation, observability, and hardware optimization.
• Build evaluation pipelines (benchmarks, regression testing, LLM-as-judge).
• Implement deep observability (tracing, latency, error tracking).
• Optimize GPU utilization (multi‑GPU serving, batching, quantization, memory tuning).
• Continuously improve throughput, latency, and cost efficiency.
• Architect and manage GPU infrastructure: model serving, load balancing, and scaling strategies.
• Hardware‑aware deployment and performance tuning.
• Build and maintain robust MLOps pipelines: model/version management, CI/CD, automated testing, and rollback strategies.
• Monitoring and feedback loops for continuous improvement.
• Engage directly with clients and stakeholders to gather and clarify business requirements.
• Translate non‑technical needs into well‑defined technical problems.
• Communicate solutions, trade‑offs, and progress through clear documentation, reports, and proposals.
• Contribute hands‑on to system design, implementation, debugging, and production incident resolution.

• Proven experience deploying LLMs in production.
• Strong experience with GPU‑based inference and optimization.
• Solid backend engineering skills (Python, APIs, distributed systems).
• Experience with MLOps and production ML systems.
• Experience with OCR/document AI and/or voice systems (STT/TTS).
• Experience with Docker and Kubernetes.
• Strong understanding of modern AI architectures (RAG, vector DBs, agent workflows).
• Experience mentoring or leading engineers.
• Strong communication skills with the ability to bridge business and technical domains.

• Experience with open‑weight models (Qwen, Llama, Deep Seek, Gemma).
• Experience with on‑prem / sovereign AI deployments.
• Experience with LoRA / fine‑tuning.
• Multilingual or Arabic NLP experience.