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.

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