AI Engineer

Job DescriptionRequired 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 QualificationsMaster'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 proficiencyJob 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 Show more Show less