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Open nowPosted 52 days ago

AI Engineer

azmtalent20 open roles

Where
Riyadh, Riyadh Province, Saudi Arabia
Work mode
On site
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Your applicationOpen nowAI Engineerazmtalent · Riyadh, Riyadh Province, Saudi Arabia

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7.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 3.4%3 days
  3. 7.8%7 days
  4. 14.3%14 days
  5. 33.7%30 days
This job: posted 52 days ago

The posting

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

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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