About the Job:
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Overview:
The company, an ADX-listed public company majority-owned by Abu Dhabi company G42, is a leading big data analytics company harnessing the power of Artificial Intelligence (AI). We deliver business solutions and societal benefits through our advanced computer vision, AI, and omni-analytics platforms, enabling insights that influence policy and foster sustainable societies.
The Opportunity:
We are looking for a highly skilled LLM Ops Engineer to lead the deployment, scaling, monitoring, and optimization of large language models (LLMs) across various environments. This role is essential to ensuring our machine learning systems are production-ready, high-performing, and resilient. The ideal candidate will have significant expertise in Python programming, a thorough understanding of LLM internals, and hands-on experience with various frameworks and deployment strategies. You will have the chance to work with cutting-edge AI technologies in a dynamic and collaborative setting.
Responsibilities:
- Design, deploy, and scale LLM infrastructure across cloud and on-premises environments.
- Build and optimize inference pipelines for low-latency, high-throughput model serving.
- Manage CI/CD pipelines and ensure secure, production-ready deployment of models.
- Deploy and maintain various AI frameworks and LLM gateways.
- Monitor performance and implement optimization strategies for resource efficiency.
- Collaborate with data scientists and engineers to operationalize models.
- Develop and maintain documentation and runbooks for operational consistency.
- Stay updated on emerging LLM techniques and best practices.
- Troubleshoot and resolve production issues to improve stability and scalability.
Qualifications:
- Bachelor’s or Master’s degree in computer science, machine learning, or a related field.
- 2+ years of experience in ML Ops, DevOps, or ML infrastructure, specifically in production deployment of ML/LLM workloads.
- Strong Python and scripting skills, experience with containerization and orchestration (Docker, Kubernetes).
- Expertise in GPU cluster management and scalable architectures.
- Proficiency with cloud/hybrid environments (AWS, GCP, Azure).
Preferred Qualifications:
- Experience with agentic AI frameworks and LLM-tool integrations.
- Familiarity with LLM orchestration and distributed inference frameworks.
- Expertise in hardware sizing, capacity planning, and infrastructure-as-code tools.
- Hands-on experience with LLM optimization techniques.
- Understanding of compliance and governance standards for operational AI systems.
Language Requirements:
English proficiency is a must.
If you are a performance-driven individual with the ability to adapt to ambiguity and a passion for innovative solutions, we would love to hear from you.
