Senior Machine Learning Engineer / Technical Lead

PhazeRo; Oman; On-site; OMR 40,000 - 55,000; Full time

Job summary

PhazeRo in Oman seeks a Senior ML Engineer to lead the development of production-grade AI systems. This role combines technical ownership and leadership of junior engineers while engaging with stakeholders to shape AI solutions.

The ideal candidate will have proven experience with LLMs, GPU optimization, and backend engineering skills in Python. You will be responsible for designing, deploying, and operating reliable AI systems with a focus on performance.

Qualifications

• Proven experience deploying LLMs in production.
• Strong experience with GPU-based inference and optimization.
• Solid backend engineering skills (Python, APIs, distributed systems).

Responsibilities

• Lead and mentor a team of junior ML engineers.
• Design, deploy, and operate scalable AI systems.
• Engage directly with clients to gather and clarify business requirements.

Skills

Deployment of LLMs in production

GPU-based inference optimization

Backend engineering (Python, APIs)

Experience with MLOps

Experience with OCR/document AI

Experience with Docker and Kubernetes

Strong communication skills

Tools

Docker

Kubernetes

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

Qualifications

• 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