AI Engineer

Required Qualifications:

Bachelors degree in Software Engineering Computer Science or a related field.

68 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:

Masters 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