Ai/ml/llm Systems Engineer - Enterprise Ai Platform Engineer
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About the Role
The role involves deploying and optimizing AI/ML systems, managing infrastructure, and implementing CI/CD pipelines for enterprise AI platforms.
Key Skills for This Role
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Overview
We are seeking an AI/ML/LLM Systems Engineer to join our Digital & AI Center of Excellence and contribute to the development of enterprise-scale AI platforms that support advanced machine learning and language model inference across Saudi Aramco's operations.
The Digital & AI Center of Excellence is responsible for delivering scalable, secure, and high-performance AI/ML/LLM systems that drive innovation and operational efficiency.
In this role, you will design and maintain infrastructure for deploying and optimizing large language models (LLMs) and vision models, hosted on NVIDIA SuperPods/ Cloud and containerized environments.
Your primary role is to ensure the efficient and scalable operation of AI models within enterprise platforms.
You will be responsible for deploying, monitoring, and optimizing inference workloads, integrating vector and relational databases, and implementing orchestration and DevOps pipelines to support continuous model improvement and delivery.
Duties & Responsibilities
- As the successful candidate, you will be required to perform the following:
- Deploy and manage LLMs and vision models on NVIDIA SuperPods, Cloud, ensuring high performance and efficient use of GPU resources.
- Build and maintain scalable inference pipelines using Kubernetes (K8s), Docker, and OpenShift for enterprise AI platforms.
- Optimize inference performance through multiple techniques.
- Benchmark and evaluate LLMs for performance, accuracy, latency, and resource utilization across different hardware and software configurations.
- Implement and support LLMOps frameworks with full observability, including logging, tracing, and model performance tracking.
- Integrate and manage vector databases (Elasticsearch) and relational databases (PostgreSQL) for efficient data retrieval and user interaction history tracking.
- Implement and maintain CI/CD (Continuous Integration/Continuous Delivery) pipelines for model and platform updates using Git, Bitbucket, Jenkins, and ArgoCD.
- Ensure high availability and reliability of AI application workflows using frameworks like Haystack.
- Collaborate with infrastructure teams on GPU provisioning and resource allocation for AI workloads.
- Develop and maintain monitoring, alerting, and dashboarding systems for AI/ML workloads to ensure SLA/SLO compliance.
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