Riyadh, Riyadh, Saudi Arabia
Salary not specified
Posted
Full-time
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Job Description

Job Description 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 Job 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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Riyadh, Saudi Arabia
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