# AI Engineer at Saudi AZM

> Onsite · Saudi Arabia · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/recruitee%3Aazmtalent%3A2707112 (read 2026-11-08).

## Facts (observed)

- Company: [Saudi AZM](https://alderhq.app/companies/saudi-azm)
- Location text: Riyadh, Riyadh Province, Saudi Arabia
- Where it can be done from (inferred): Onsite · Saudi Arabia (evidence: "Location: Riyadh, Riyadh Province, Saudi Arabia")
- Posted: 2026-08-11 (89 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://azmtalent.recruitee.com/o/ai-engineer
- Skills listed (inferred): Python, FastAPI, GCP, Azure, Kubernetes, Docker, Terraform, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### Required Qualifications

Bachelor's degree in Software Engineering, Computer Science, or a related field.

6–8+ years in software, DevOps, or platform engineering, including at least 2 years in an applied AI or ML engineering capacity.

Proven delivery of production AI/LLM systems — not only research or notebook-stage work.

Strong Python; comfortable with Bash and YAML.

Deep hands-on experience with Kubernetes, Docker/Podman, and Terraform.

Production experience with at least one 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 a team and setting engineering standards across multiple squads.

### Preferred Qualifications

Master's degree in Applied AI, Machine Learning, or a related discipline.

Fine-tuning experience with QLoRA/LoRA on GPU clusters; PyTorch and Transformers.

Vector database experience (Milvus, Pinecone, or Weaviate) and RAG retrieval design.

Experience delivering on Saudi government or large-scale national digital platforms, with familiarity in local compliance and standards.

Arabic and English professional proficiency

### Requirements

### AI systems

Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models such as Llama-3 and Mistral).

Design reproducible evaluation harnesses and A/B test frameworks with tracked metrics: task success rate, safety rate, and latency distributions (p50/p95).

Architect multi-agent and RAG systems (LangGraph, FastAPI, vector databases) from prototype through production.

Implement safety guardrails — input/output validation, allowlist/denylist policies, and controls that reduce invalid or high-risk model actions.

Translate business use cases into deployable prototypes with measurable acceptance criteria, and demo them to stakeholders.

### Platform & infrastructure

Design and operate cloud infrastructure and MLOps workspaces (Azure, OCI, or GCP) for AI workloads on Kubernetes and containerized runtimes.

Build CI/CD pipelines and GitOps-based release promotion (Argo CD) across development, test, and production environments.

Implement end-to-end observability (Azure Monitor, Application Insights, ELK) with defined detection and response targets.

Apply network and perimeter security baselines (FW/WAF), automated code quality and SCA scanning (SonarQube, Black Duck), and gated pipelines.

Own disaster recovery design — automated backups, failover, and documented RTO/RPO commitments.

### Engineering leadership

Lead and mentor a cloud/AI operations team; define monitoring, incident response, and release governance practices with clear uptime and MTTR targets.

Standardize SDLC practices — branching strategy, PR governance, release management, delivery reporting — to improve lead time and deployment frequency.

Consolidate engineering tooling and workflows; drive migrations and platform standardization where fragmentation slows delivery.

Produce handover documentation and runbooks that make systems auditable and operationally transferable.

Support vendor and licensing negotiations for cloud enterprise agreements

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Aazmtalent%3A2707112 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.