Senior Machine Learning Engineer

Employment; Muscat; On-site; OMR 58,000 - 73,000; Full time

Job summary

Employment in Muscat is seeking a hands-on senior ML engineer to lead production-grade AI systems spanning LLMs, OCR, and voice, with technical ownership and leadership of a high-performing team of junior engineers. You will engage directly with stakeholders to shape AI solutions and drive robust implementation.

You will design, deploy, and operate scalable AI systems, own end-to-end model performance, and optimize for latency, throughput, and cost on GPU infrastructure.

Qualifications

• Proven experience deploying LLMs in production.
• Strong experience with GPU-based inference and optimization.
• 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 bridging business and technical domains.

Responsibilities

• Lead and mentor a team of junior ML engineers through code/design reviews and best practices.
• Design, deploy, and operate scalable AI systems with reliability and performance.
• Lead production deployment of LLMs and multimodal systems (RAG, OCR, voice).
• Own model performance end-to-end with evaluation, observability, and hardware optimization.
• Architect and manage GPU infrastructure including serving, scaling, and tuning.
• Build and maintain robust MLOps pipelines with CI/CD and testing.
• Engage with clients to translate business needs into technical solutions and document progress.
• Contribute hands-on to system design, debugging, and incident resolution.

Skills

LLM deployment

GPU optimization

MLOps

OCR/Voice AI

Docker & Kubernetes

AI architectures

Team leadership

Communication

Tools

Docker

Kubernetes

Job description

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
• 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