Full Stack Machine Learning Engineer (Data Centre AI Engineering) | Riyadh, Saudi Arabia

Location

Riyadh, Saudi Arabia

Job Category

• Information Technology (IT) & Software
• Engineering & Technical
• Science & Research
• Telecommunications
• Others / Miscellaneous

Job Overview

Qualcomm is seeking a Full Stack Machine Learning Engineer to support AI platform engineering and AI solution development for inference workloads and rack-scale data centre deployments in Riyadh, Saudi Arabia. The role combines full-stack software engineering, machine learning, distributed systems, high-performance computing, and infrastructure engineering to deliver scalable AI services and platforms.

The successful candidate will design and optimize API serving layers, develop agentic workflows and Retrieval-Augmented Generation (RAG) pipelines, build model fine-tuning and lifecycle management processes, and contribute to AI Inference Suite SDKs and tooling. The role also covers Kubernetes orchestration, infrastructure-as-code, cluster management, telemetry, observability, monitoring, and data centre resource management.

This position offers significant career growth and professional development opportunities for engineers working at the intersection of AI, machine learning, cloud-native infrastructure, and data centre engineering. Exposure to LLM runtimes, GenAI optimization, rack-scale orchestration, high-performance computing, and advanced networking can support long-term specialization in AI infrastructure and platform engineering.

Key Responsibilities

• Build and optimize API serving layers for AI inference workloads.
• Develop intelligent agents and RAG pipelines using frameworks such as LangChain and CrewAI.
• Implement production-grade bring-your-own-model and fine-tuning workflows.
• Manage dataset ingestion, orchestration, model evaluation, and deployment pipelines.
• Integrate and optimize LLM runtimes including vLLM, Dynamo, and llm-d.
• Contribute to AI Inference Suite SDKs using Python, TypeScript, Java, and Rust.
• Develop CLI tools and reference applications for AI platforms.
• Design and maintain AI cluster management software for provisioning, orchestration, and monitoring.
• Implement telemetry and observability using Prometheus and OpenTelemetry.
• Integrate out-of-band data centre management technologies including Redfish and IPMI.
• Develop Infrastructure-as-Code workflows using MAAS, Terraform, and Ansible.
• Enable Kubernetes and Helm-based orchestration for inference clusters and multi-tenant environments.
• Build dashboards for rack health, inventory, and SLA compliance.
• Support bare-metal and containerized deployments.
• Apply machine learning and software engineering principles to distributed systems and high-performance computing environments.
• Stay current with GenAI developments, AI orchestration technologies, and data centre engineering best practices.

Requirements & Qualifications

Education

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
• A Master’s degree in Computer Science, Machine Learning, or a related discipline is preferred.
• PhD candidates in relevant technical disciplines may also be considered.

Experience

• 5+ years of software engineering experience.
• 3+ years of experience in machine learning or high-performance computing environments.
• Equivalent experience may be considered where candidates demonstrate the required competencies and ability to perform the role.

Technical Skills

• Strong programming skills in Python.
• Proficiency in Rust or Go.
• Strong TypeScript development capabilities.
• Strong understanding of data structures and algorithms.
• Experience with distributed systems and high-performance computing.
• Hands-on experience with Kubernetes and Helm.
• Experience with Prometheus and OpenTelemetry.
• Strong knowledge of Ansible and Terraform.
• Practical experience with LLM runtimes and agent frameworks.
• Experience with AI inference and rack-scale orchestration.

Preferred Skills & Experience

• Experience building AI inference and model fine-tuning pipelines.
• Experience developing agentic workflows.
• Knowledge of data centre resource lifecycle management.
• Experience with Redfish/IPMI and MAAS/OpenStack.
• Exposure to scale-up data centre networking technologies such as RoCE, RDMA, or NVLink.
• Experience with inference and GenAI model performance optimization.
• Familiarity with containerized and bare-metal deployments.

Salary, Benefits & Career Growth

The position offers a salary package including housing and transport allowances, along with additional compensation and employee benefits.

Benefits

• Stock awards (RSUs).
• Performance-related bonus.
• Employee stock purchase scheme.
• 16 weeks of fully paid maternity leave.
• 6 weeks of fully paid paternity leave.
• Child education allowance.
• Relocation and immigration support, where required.
• Life and medical insurance.
• Live+ Well reimbursement for health and recreational membership fees.

Career Growth & Professional Development

The role provides opportunities to develop advanced expertise in machine learning engineering, AI infrastructure, GenAI, LLM inference, Kubernetes, distributed systems, and data centre AI engineering. Engineers can expand their professional capabilities through hands-on work with emerging AI technologies, inference optimization, cloud-native orchestration, observability, and high-performance computing.

Training, certification, and skill-development opportunities may be available through the employer’s professional development programs.

Application Process

Application Process (Website)

Apply through the official job posting.

Click Apply Now on the website.

Official Job Posting: Full Stack Machine Learning Engineer – Qualcomm | LinkedIn Jobs

HR Email for Application

No general HR email for job applications was provided in the supplied job description. The disability accommodations email mentioned in the posting is specifically for accommodation requests and should not be used for CV or application inquiries.