Job description
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.
Responsibilities: * Lead and mentor a team of highly talented junior ML engineers through: * Code reviews, design reviews, and technical direction * Enforcement of 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 Nice to Have: * Experience with open-weight models (Qwen, Llama, DeepSeek, Gemma) * Experience with on-prem / sovereign AI deployments * Experience with LoRA / fine-tuning * Multilingual or Arabic NLP experience
This job post has been translated by AI and may contain minor differences or errors.
