AI/ML/LLM Systems Engineer - Enterprise AI Platform Engineer

Apply now » Title: AI/ML/LLM Systems Engineer - Enterprise AI Platform Engineer Job Req ID: 17810 Description: Aramco energizes the world economy. Aramco occupies a special position in the global energy industry. We are one of the world’s largest producers of hydrocarbon energy and chemicals, with among the lowest Upstream carbon intensities of any major producer. With our significant investment in technology and infrastructure, we strive to maximize the value of the energy we produce for the world along with a commitment to enhance Aramco’s value to society. Headquartered in the Kingdom of Saudi Arabia, and with offices around the world, we combine market discipline with a generations’ spanning view of the future, born of our nine decades experience as responsible stewards of the Kingdom’s vast hydrocarbon resources. This responsibility has driven us to deliver significant societal and economic benefits to not just the Kingdom, but also to a vast number of communities, economies, and countries that rely on the vital and reliable energy that we supply. We are one of the most profitable companies in the world, as well as amongst the top five global companies by market capitalization. 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. Minimum Requirements As a successful candidate you will: Hold a Master’s degree in Computer Science, Software Engineering, or a related field Have 8 years of experience in AI/ML systems or cloud-native infrastructure, including at least 4 years in LLM deployment and optimization. Proficiency in Python and SQL is required, with experience in building and optimizing AI/ML applications. Ability to work with Kubernetes (K8s), Docker, and OpenShift in production environments. Experience deploying and optimizing LLMs and vision models on NVIDIA GPU clusters and high-performance computing (HPC) environments and Cloud environment. Ability to demonstrate proficiency in inference scaling, distributed computing, and SLA/SLO planning for AI workloads. Strong knowledge in Elasticsearch, PostgreSQL, and workflow frameworks like Haystack for AI application development. Ability to implement CI/CD pipelines using tools like Git, Bitbucket, Jenkins, and ArgoCD. Experience in benchmarking and evaluating LLMs for performance, accuracy, and efficiency is required. Monitoring and dashboarding for AI/ML systems is also necessary. Work Location and Work Schedule Work Location: Within Saudi Arabia – To be specified in Job offer Work Schedule: Full Time - To be specified in Job offer Job Post Duration Job posting start date: 04/28/2026 Job posting end date: 04/28/2027 Working environment Our high-performing employees are drawn by the challenging and rewarding professional, technical a

Hold a Master’s degree in Computer Science, Software Engineering, or a related field. Have 8 years of experience in AI/ML systems or cloud-native infrastructure, including at least 4 years in LLM deployment and optimization. Proficiency in Python and SQL, with experience in building and optimizing AI/ML applications. Ability to work with Kubernetes (K8s), Docker, and OpenShift in production environments. Experience deploying and optimizing LLMs and vision models on NVIDIA GPU clusters and high-performance computing (HPC) environments and Cloud environment. Ability to demonstrate proficiency in inference scaling, distributed computing, and SLA/SLO planning for AI workloads. Strong knowledge in Elasticsearch, PostgreSQL, and workflow frameworks like Haystack for AI application development. Ability to implement CI/CD pipelines using tools like Git, Bitbucket, Jenkins, and ArgoCD. Experience in benchmarking and evaluating LLMs for performance, accuracy, and efficiency is required. Monitoring and dashboarding for AI/ML systems is also necessary.

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.