AI Engineer

**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. **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. **Technical Environment** Python, FastAPI, PyTorch, Transformers, LangGraph, Milvus/Pinecone/Weaviate, Redis, PostgreSQL, Kubernetes, Docker/Podman, Terraform, Argo CD, Azure DevOps, GitHub Actions, Azure ML, Azure Monitor / Application Insights, ELK, SonarQube, Black Duck, Fortinet FW/WAF.