AI Engineer – LLM & MLOps Specialist

About the role

The AI Engineer will lead the design, development, and productionisation of large‑language‑model (LLM) solutions for domain‑specific tasks. You will work closely with business stakeholders to translate use cases into robust, measurable AI prototypes and bring them to operational scale on cloud platforms.

Key responsibilities

• Build, fine‑tune, and evaluate LLM systems (e.g., Llama‑3, Mistral) using QLoRA/PEFT on GPU clusters.
• Design reproducible evaluation harnesses and A/B testing frameworks with metrics such as task success rate, safety rate, and latency distributions.
• Architect multi‑agent and Retrieval‑Augmented Generation (RAG) pipelines using LangGraph, FastAPI, and vector databases.
• Implement safety guardrails, including input/output validation, allow‑list/deny‑list policies, and risk‑mitigation controls.
• Design, deploy, and operate cloud‑native MLOps workspaces on Azure, OCI, or GCP using Kubernetes, Docker/Podman, and Terraform.
• Build CI/CD pipelines and GitOps‑based release promotion (Argo CD) across dev, test, and prod environments.
• Implement end‑to‑end observability (Azure Monitor, Application Insights, ELK) and automated security baselines (FW/WAF, SonarQube, Black Duck).
• Own disaster‑recovery design, including automated backups, failover, and documented RTO/RPO commitments.
• Lead and mentor a cloud/AI operations team, defining monitoring, incident response, and release‑governance practices.
• Standardise SDLC practices, consolidate tooling, drive platform migrations, and produce handover documentation and runbooks.

Required profile

• Bachelor's degree in Software Engineering, Computer Science or related field.
• 6–8+ years of software, DevOps or platform engineering experience, with at least 2 years in applied AI/ML engineering.
• Proven delivery of production AI/LLM systems beyond research notebooks.
• Strong Python skills; comfortable with Bash and YAML.
• Deep hands‑on experience with Kubernetes, Docker/Podman, and Terraform.
• Production experience on a major cloud (Azure preferred; OCI or GCP acceptable).
• Demonstrated ownership of CI/CD at scale (Azure DevOps, GitHub Actions) and GitOps release models.
• Experience leading engineering teams and setting standards across multiple squads.
• Preferred: Master's in Applied AI/ML, fine‑tuning experience with QLoRA/LoRA, vector‑DB expertise (Milvus, Pinecone, Weaviate), and familiarity with Saudi government digital platform compliance.
• Professional proficiency in Arabic and English.

Required skills

• Python
• Bash
• YAML
• Kubernetes
• Docker
• Podman
• Terraform
• Azure
• OCI
• GCP
• Azure DevOps
• GitHub Actions
• Argo CD
• Azure Monitor
• Application Insights
• ELK
• SonarQube
• Black Duck
• QLoRA / LoRA
• PyTorch
• Transformers
• Milvus
• Pinecone
• Weaviate
• LangGraph
• FastAPI