Ai/ml/llm Systems Engineer

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.

Responsibilities

• 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 and 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.

Qualifications

• 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.