AI Engineer

Job Overview

This role involves designing, building, and deploying advanced AI and large language model (LLM) systems tailored to domain-specific applications, utilizing technologies like QLoRA and PEFT on open-weight models such as Llama-3 and Mistral. The engineer will build scalable evaluation frameworks, including A/B testing with metrics such as task completion rates, safety assessments, and latency benchmarks, as well as architect multi-agent systems and retrieval-augmented generation architectures using LangGraph, FastAPI, and vector databases.

Responsibilities

• Develop, fine-tune, and assess LLM-based AI models for specialized tasks.
• Create reproducible evaluation systems and A/B testing frameworks that track success, safety, and latency metrics.
• Design and implement multi-agent and retrieval-augmented architectures from initial prototypes to production deployment.
• Establish and maintain safety mechanisms including input/output validations, allowlists and denylists, and other controls to prevent risky AI behaviors.
• Translate business needs into functional prototypes with clear acceptance criteria and present demonstrations to stakeholders.
• Design, manage, and operate cloud infrastructure and MLOps environments using platforms like Azure, OCI, or GCP on Kubernetes and containerized runtimes.
• Create CI/CD pipelines with GitOps release strategies (Argo CD) supporting development through production stages.
• Implement comprehensive monitoring and observability with Azure Monitor, Application Insights, and ELK stack, defining detection and response goals.
• Enforce network security and perimeter controls via firewalls/WAFs and integrate automated code quality and security scanning (SonarQube, Black Duck) with gated pipeline workflows.
• Develop disaster recovery processes including automated backups, failover plans, and documented recovery time and point objectives.
• Provide leadership and mentorship to cloud and AI operations teams, shaping monitoring, incident management, and release governance to meet uptime and mean time to recovery targets.
• Standardize software development lifecycle practices including branching, pull request governance, release management, and delivery reporting to enhance deployment frequency and reduce lead times.
• Consolidate engineering tools and workflows, championing platform migrations and standardization to eliminate delivery bottlenecks.
• Produce thorough handover documentation and operational runbooks to ensure systems are auditable and easily transferable.
• Assist in vendor relations and cloud licensing negotiations.

Qualifications

• Minimum bachelor's degree in Software Engineering, Computer Science or equivalent field; a master's in Applied AI or related fields is preferred.
• At least 6 to 8 years of experience in software, DevOps, or platform engineering, with a minimum of 2 years working directly with applied AI or machine learning systems in production.
• Proven track record delivering robust, production-ready AI/LLM solutions beyond experimental stages.
• Proficiency in Python programming, along with familiarity in Bash scripting and YAML configurations.
• Extensive hands-on expertise in Kubernetes, Docker or Podman container technologies, and Terraform infrastructure as code.
• Experience managing cloud services, particularly Azure (preferred), OCI, or GCP.
• Demonstrated ownership and operation of large-scale CI/CD systems using Azure DevOps or GitHub Actions, including GitOps methodologies.
• Leadership capabilities with experience guiding engineering teams and standardizing technical processes across squads.
• Preferred skills include fine-tuning large models using QLoRA/LoRA on GPUs, working knowledge of PyTorch, Transformers libraries, as well as vector database technologies such as Milvus, Pinecone, or Weaviate and retrieval-augmented generation designs.
• Experience delivering AI projects for Saudi government or sizable national digital platforms, with knowledge of local compliance and standards.
• Fluency in both Arabic and English at a professional level.

Technical Environment

The role extensively uses Python, FastAPI, PyTorch, Transformers, LangGraph, vector databases (Milvus, Pinecone, Weaviate), Redis, PostgreSQL, Kubernetes, Docker/Podman, Terraform, Argo CD, Azure DevOps, GitHub Actions, Azure ML, Azure Monitor, Application Insights, ELK stack, SonarQube, Black Duck, and Fortinet firewall/WAF security tools.