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

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

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