Senior Machine Learning Engineer​/Technical Lead

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 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

Phaze Ro

Requirements

• 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